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A Simple and Reliable Photovoltaic Forecast for Reliable Power System Operation Control
10th IFAC Symposium on 4-6, Control Tokyo, Japan, September 2018of Power and Energy Systems Tokyo, Japan, September 2018of 10th Symposium on Control 10th IFAC IFAC Symposium on 4-6, Control of Power Power and and Energy Energy Systems Systems Available online at www.sciencedirect.com Tokyo, Tokyo, Japan, Japan, September September 4-6, 4-6, 2018 2018
ScienceDirect IFAC PapersOnLine 51-28 (2018) 161–166
A A Simple Simple and and Reliable Reliable Photovoltaic Photovoltaic Forecast Forecast for for Reliable Reliable Power Power System System Operation Operation A Simple and Reliable Photovoltaic Forecast for Reliable Power System Operation Control Control Control
L. Ma **, N. Yorino *, Y. Sasaki *, Y. Zoka *, K. Khorasani **, A.B. Rehiara * L. Ma **, N. Yorino *, Y. Sasaki *, Y. Zoka *, K. Khorasani **, A.B. Rehiara * L. **, *, Y. Sasaki Khorasani **, A.B. Zoka L. *Ma Ma **, N. N. Yorino Yorino Sasaki *, *, Y. Y. Zoka *, *, K. K. Khorasani **,Kagamiyama, A.B. Rehiara Rehiara ** Graduate School *, of Y. Engineering, Hiroshima University, 1-4-1 * Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, 739-8527 JapanHiroshima (Contact email: [email protected]) Graduate School School of of Engineering, University, 1-4-1 Kagamiyama, Kagamiyama, Higashi-Hiroshima, 739-8527 JapanHiroshima (Contact email: [email protected]) ** Graduate Engineering, University, 1-4-1 ** Department of Electrical and Computer Engineering, Concordia University, Higashi-Hiroshima, 739-8527and Japan (Contact email: [email protected]) [email protected]) ** Department of Electrical Computer Engineering, Concordia University, Higashi-Hiroshima, 739-8527 Japan (Contact email: QC, 1M8 Canada (Contact email: [email protected]) **Montreal, Department ofH3G Electrical and Computer Computer Engineering, Concordia University, University, Montreal, QC, H3G 1M8 Canada (Contact email: [email protected]) ** Department of Electrical and Engineering, Concordia Montreal, QC, H3G 1M8 Canada (Contact email: [email protected]) Montreal, QC, H3G 1M8 Canada (Contact email: [email protected]) Abstract: Recently various forecasting methods for photovoltaic (PV) generation have been proposed in Abstract: Recently various forecasting methods for photovoltaic (PV) generation have been proposed in the literature. However, these standard methods cannot be successfully and widely used been in general due to Abstract: Recently various methods for (PV) proposed the literature. However, theseforecasting standard methods be successfully and widely have used been in general due in to Abstract: Recently various forecasting methodscannot for photovoltaic photovoltaic (PV) generation generation have proposed in the fact that they require access to specialized data that are not always and everywhere readily available the However, standard methods cannot used general due fact that they requirethese access to specialized thatbe aresuccessfully not always and and widely everywhere readily available the literature. literature. However, these standard methodsdata cannot be successfully and widely used in in general due to to in practice. Furthermore, prediction accuracy of such methods tends to deteriorate specially due to data the fact they to data that are always everywhere readily available in prediction accuracy of such deteriorate specially due to data thepractice. fact that that Furthermore, they require require access access to specialized specialized data thatmethods are not not tends alwaystoand and everywhere readily available scarcity. This paper proposes a simple and reliable PV forecasting method using machine learning and in Furthermore, prediction accuracy of tends to specially due scarcity. This paper proposes a simple and reliable forecasting using machine and in practice. practice. Furthermore, prediction accuracy of such suchPVmethods methods tendsmethod to deteriorate deteriorate speciallylearning due to to data data neural networks. Confidence interval (CI) results are specifically provided for the local supply-demand scarcity. This paper paper proposes interval simple(CI) andresults reliable PV forecastingprovided method for using machine learning and and neural networks. Confidence arePV specifically themachine local supply-demand scarcity. This proposes aa simple and reliable forecasting method using learning controlnetworks. as well as for the robust power systemare security. The provided proposed for method usessupply-demand only weather neural Confidence intervalpower (CI) results results specifically the local local control as well as for the robust systemare security. The provided proposed for method usessupply-demand only weather neural networks. Confidence interval (CI) specifically the forecasting data that are the provided bypower the Japan Meteorological Agency (JMA) and which isonly available to control as well well as for for robustby system security. The The proposed method uses is weather forecasting data that are the provided the Japan Meteorological Agency (JMA) and which available to control as as robust power system security. proposed method uses only weather the public. The proposed method maintains a high level of accuracy by using real-time correlation data forecasting data proposed that are are provided provided by the the Japan Japan Meteorological Agency (JMA) and which whichcorrelation is available available to the public. The method maintains a high level of accuracy by(JMA) using real-time data forecasting data that by Meteorological Agency and is to between theThe specific target and themaintains neighboring areas. Multiple neural networks are constructed based on the proposed aa high level of using correlation between theThe specific targetmethod and themaintains neighboring areas. Multiple neural by networks are constructed baseddata on the public. public. proposed method high level of accuracy accuracy by using real-time real-time correlation data a weathertheclustering technique. It has been confirmed through extensive simulation results that the between specific and areas. neural networks are on abetween weathertheclustering technique. has been confirmed through extensive simulation results based that the specific target target and the theIt neighboring neighboring areas. Multiple Multiple neural networks are constructed constructed based on proposed method demonstrates robustness in prediction accuracy and CI effectiveness. aproposed weather clustering technique. It has been confirmed through extensive simulation results that the demonstrates in prediction accuracy CI effectiveness. a weathermethod clustering technique.robustness It has been confirmed throughand extensive simulation results that the proposed method demonstrates robustness in prediction accuracy and effectiveness. © 2018, IFAC (International Federation of Automatic Control) Hosting byCI Ltd. All rights reserved. proposed method demonstrates robustness inenergy prediction accuracy andNeural CIElsevier effectiveness. Keywords: Confidence intervals, Local management, networks, PV forecasting, Keywords: Confidence intervals, Local energy management, Neural networks, PV forecasting, Uncertainties. Keywords: Confidence Confidence intervals, intervals, Local Local energy energy management, management, Neural Neural networks, networks, PV PV forecasting, forecasting, Uncertainties. Keywords: Uncertainties. Uncertainties. real-time operation and control of a small size power system, real-time operation and control of a small size power system, 1. INTRODUCTION which canoperation representand a given single building to the level of a 1. INTRODUCTION real-time control of small system, which canoperation representand a given single the level of a real-time control of aa building small size sizetopower power system, 1. INTRODUCTION remote island power network. 1. INTRODUCTION Renewable energy sources (RESs) which are not based on which represent aa given island power network. which can can represent given single single building building to to the the level level of of aa Renewable energy sources (RESs) which are not based on remote fossil fuel have attracted significant attention due to their remote island power network. power network. Renewable energy sources (RESs) are on Variousisland PV prediction methods are available in the literature fossil fuel have attracted attention duebased to their Renewable energy sourcessignificant (RESs) which which are not not based on remote Various PV prediction methods are available in the literature reduced environmental impact. Inattention particular, active fossil have due their as the day-ahead forecasting for a small-scale PV reduced impact. Inattention particular, fossil fuel fuel environmental have attracted attracted significant significant due to toactive their such are in such as PV theprediction day-aheadmethods forecasting for a small-scale PV installation of photovoltaic impact. (PV) power generation is being Various Various PV prediction methods are available available in the the literature literature reduced In particular, et al., 2015), the spatial-temporal solar short-term installation of photovoltaic impact. (PV) power being (Zhang reduced environmental environmental In generation particular, is active active such as as etthe the day-ahead forecasting for aa solar small-scale PV (Zhang al., 2015), the spatial-temporal short-term strongly promoted in Japan (Yamada, 2016). However, the such day-ahead forecasting for small-scale PV installation of power generation is (Bessa et al., and hybridsolar algorithms for strongly promoted in Japan (PV) (Yamada, However, the forecasting installation of photovoltaic photovoltaic (PV) power2016). generation is being being (Zhang et et al., al., 2015), the 2015), spatial-temporal short-term forecasting (Bessa et al., 2015), and hybrid algorithms for PV output is generally uncertain due to spatial and time(Zhang 2015), the spatial-temporal solar short-term strongly promoted in (Yamada, However, the PV prediction (Asrari et al., 2017,algorithms Yang, et al., PV output is generally uncertain due 2016). to spatial and timestrongly promoted in Japan Japan (Yamada, 2016). However, the short-term forecasting (Bessa et al., 2015), and hybrid for short-term PV prediction (Asrari et al., 2017, Yang, et al., varying changes in solar radiation. Therefore, by using forecasting (Bessa et al., 2015), and hybrid algorithms for PV is uncertain due to time2014), among others. The worket al., Matsuda et al., et2014 varying changes in solar radiation. by using PV output output is generally generally uncertain due Therefore, to spatial spatial and and time- short-term PV prediction (Asrari 2017, Yang, al., 2014), among others. The work Matsuda et al., 2014 controllable generator groups that correspond to existing short-term PV prediction (Asrari et al., 2017, Yang, et al., varying in radiation. Therefore, using a PV others. prediction method for localet distribution controllable generator groups that correspond varying changes changes in solar solar radiation. Therefore,toby byexisting using proposes 2014), The work al., a PV others. prediction for localet distribution thermal power and/or diesel generators, it would to be possible 2014), among among The method work Matsuda Matsuda al., 2014 2014 controllable generator groups that correspond correspond existing proposes control system based on the correlation analysis of the thermal power and/or diesel generators, it would to be possible controllable generator groups that existing proposes a PV prediction method for local distribution control system based on the correlation analysis of the to achieve a stable power supply based on well-suited proposes a PV prediction method for local distribution thermal power and/or diesel generators, it would would be possible observed data. Other methods use cloud images observed to achieve a and/or stable diesel powergenerators, supply based on be well-suited thermal power it possible control system based on the correlation analysis of the observed data. Other methods use cloud images observed generation planning, operation and control policies. control system based on the correlation analysis of the to aa stable power based on the ground (Yamamoto et al., 1999) orimages that are observed obtained generation and control policies. to achieve achieveplanning, stableoperation power supply supply based on well-suited well-suited from observed data. Other methods use cloud from the ground (Yamamoto et al., 1999) or that are obtained observed data. Other methods use cloud images observed generation planning, control weather satellites (Simose et al., 2014). However, these generation planning, operation and control policies. On the other hand, ifoperation one can and readily and policies. accurately predict from ground (Yamamoto et 1999) or are weather satellites (Simose et al., 2014). However, these from the the ground (Yamamoto et al., al., 1999) or that that are obtained obtained On the other hand, if one can readily and accurately predict from complex and time-consuming methods require access and the amount of solar radiation it would be possible to then from weather satellites (Simose et al., 2014). However, these complex and time-consuming methods require access and from weather satellites (Simose et al., 2014). However, these On the other hand, if one can readily and accurately predict the amount solarif radiation it would possible to then availability of special data such as the meteorological image On the otherofhand, one can readily andbeaccurately predict make efficient operational plans for both electric power complex and time-consuming methods require access and availability of special data such as the meteorological image complex and time-consuming methods require access and the amount of solar radiation it would be possible to then make efficient operational plans for both electric power the amount of solar radiation it would be possible to then data, solar data from the radiation meters at power grid storages as well as thermal and hydraulic generators. In availability of special data such as the meteorological image solar of data fromdata the such radiation at powerimage grid availability special as themeters meteorological make efficient efficient operational plans for both electric electric power storages as welloperational as thermalplans and for hydraulic generators. In data, make both power substations, among others that are not readily available in addition, by well analyzing the prediction error andgenerators. its statistical solar from the radiation meters at power substations, among others are not readily in data, solar data data from the that radiation meters at available power grid grid storages as and In addition, by well analyzing the prediction error andgenerators. its statistical storages as as as thermal thermal and hydraulic hydraulic In data, practice. Furthermore, these methods focus their attention on distribution, it would bethe possible to maintain a proper reserve substations, among are readily in Furthermore, thesethat methods focus their available attention on substations, among others others that are not not readily available in addition, by prediction error its distribution, would bethe possible to maintain a proper reserve practice. addition, by itanalyzing analyzing prediction error and and its statistical statistical the accuracy of obtained predictions and there areattention practically margin for a stable supply-demand control policy. practice. Furthermore, these methods focus their on the accuracy of obtained predictions and there are practically practice. Furthermore, these methods focus their attention on distribution, be to aa proper margin for it a stable supply-demand policy. no examination conducted on the CI metric. distribution, it would would be possible possible to maintain maintain control proper reserve reserve Consequently, in addition to developingcontrol an accurate accuracy obtained and no on the CI metric. theexamination accuracy of of conducted obtained predictions predictions and there there are are practically practically margin supply-demand policy. Consequently, addition to developingcontrol an accurate margin for for aa in stable stable supply-demand policy. the prediction technique, development of advanced energy no examination conducted on the CI metric. no examination conducted on the CI metric. Consequently, in addition to developing an accurate prediction technique, development of advanced energy This paper proposes a PV forecasting methodology by Consequently, in addition to developing an accurate This paper proposes a PV forecasting methodology by management technology is regarded as an important prediction development of advanced energy neural networks (NNs) that are trained based onbya management technology is regarded an important prediction technique, technique, development of as advanced energy utilizing This paper paper proposes PV forecasting forecasting methodology utilizing neural networks that are trained based onbya objective and goal to be accomplished by researchers. This proposes aa (NNs) PV methodology management technology is as amount of meteorological information thatbased are readily objective and goal to be accomplished by researchers. management technology is regarded regarded as an an important important limited utilizing neural networks (NNs) that are trained on limited amount of meteorological information that are readily utilizing neural networks (NNs) that are trained based on aa objective and goal to be accomplished by researchers. available from local areas. The proposed method can predict objective and goal to be accomplished by researchers. In this paper, a “supply-demand manager” module as well as limited amount of meteorological information that are readily from local areas. The proposed method predict limited amount of meteorological information that can are readily In this paper, a “supply-demand manager” module as well as available the day-ahead as well as real-time PV outputs and directly al.,well 2012, aInsimulator is aproposed as shownmanager” in Fig. 1 (Hafiz from local areas. The method can predict the day-ahead well as real-time PV outputs available from as local areas. The proposed proposed methodand can directly predict this paper, paper, “supply-demand moduleet as as available et as al.,well 2012, aInsimulator is aproposed as shownmanager” in Fig. 1 (Hafiz this “supply-demand module as evaluates the CIaserror that is useful PV for devising the directly supplyYorino et al., 2012, Sasaki et al., 2017). The supply-demand the day-ahead well as real-time outputs and evaluates the CI error that is useful for devising the supplyday-ahead as well as real-time PV outputs and directly (Hafiz et aa simulator is as Fig. Yorino et al., 2012, Sasaki et al., in 2017). supply-demand (Hafiz et al., al., 2012, 2012, the simulator is proposed proposed as shown shown in Fig. 11The control policies. manageretmodule is Sasaki developed for2017). operational planning and demand the error demand evaluatescontrol the CI CIpolicies. error that that is is useful useful for for devising devising the the supplysupplyYorino al., et The manageretmodule is Sasaki developed for2017). operational planning and evaluates Yorino al., 2012, 2012, et al., al., The supply-demand supply-demand demand control policies. demand control policies. manager module is developed for operational planning and manager module is developed for operational planning and Copyright © 2018 IFAC 161 2405-8963 © 2018, IFAC (International Federation of Automatic Control) Copyright 2018 IFAC 161 Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. Copyright © © 2018 2018 IFAC IFAC 161 Copyright 161 10.1016/j.ifacol.2018.11.695
L. Ma et al. / IFAC PapersOnLine 51-28 (2018) 161–166
To summarize, our main contributions are stated as follows: (1) The accuracy of our proposed PV prediction scheme is shown to be sufficiently and acceptably high despite the fact that the method uses only a quite general set of data that are commonly available even in disadvantaged areas, and (2) Specific CI results are explicitly provided for use in local supply-demand control policy to realize a robust power system security. The second contribution above is realized by combination and integration of the following new techniques as described in detail subsequently. Specifically, based on the classification of weather forecast, a cluster of NNs are constructed that are dedicated to the individual forecasted weathers to achieve a high and accurate prediction performance. The correlation between the target area and the neighboring regions is also utilized. A solar radiation correlation analysis (SRCA) technique is then proposed and applied to the problem of real-time prediction. Specifically, the concept of “time distance” is introduced to take into account spatial-temporal elements with NNs. Effectiveness and accuracy of our proposed method is then demonstrated by using the weather data that are provided by the Japan Meteorological Agency (JMA), and the solar radiation meter data that are provided by the Ministry of Economy, Trade and Industry (Ministry of Economy, Trade and Industry, website).
Online Database
Offline Database Past Electricity Demand, Weather Information, Specification of Generator (Gen.) and Storage Battery (BT), Network Information, etc.
WT
Real-time Demand and Weather Information, Gen. and BT Operation Information, Network Operation Information, etc.
BT
PV Day ~ Hour order Day-ahead Planning Manager
Hour ~ Min. order Real-time Operation Manager
Day-ahead Forecast
Min. ~ Sec. order Real-time Control Manager
Real-time Forecast
・Electricity Demand ・Photovoltaic (PV) Gen. ・Wind-turbine (WT) Gen. > Prediction Errors > Confidence Intervals (CIs)
Normal State Control
・Electricity Demand ・PV, WT > Covariance matrices > CIs
・Load Frequency Control (LFC) ・Area requirement evaluation
Gen. Dispatching
・Gen. shedding ・Load control (shedding)
Gen. Scheduling
〜
Customer
Governor
Emergency Control
・Stochastic dynamic economic load dispatch using robust time-sequence dynamic feasible region (RTDF) > Real-time Gen. schedule > BT re-scheduling > Supply and Demand Mismatch (SDM)
・Unit Commitment (UC) > Gen. Start/Stop schedule > BT operation patterns > Reserve Management
〜
Customer
Governor
Re-dispatching
・Gen./BT re-dispatching
〜
Customer
Governor
Information Board > = <
SDM
Fig. 1. Proposed supply & demand management module. cluster of weather-dedicated NNs for each target time is proposed and developed. 2.2 Real-time Energy Management Operation Module
On the specific target day, a real-time frequency control is performed and performance including prediction results, operational planning and real-time operation are all evaluated by simulations associated with the frequency variations. 3. DAY-AHEAD PV FORECAST METHOD Figure 2 depicts the general methodology of our proposed day-ahead PV generation forecast scheme which provides a 24-hour PV prediction having a 30-minute resolution. The forecasting process is conducted in the following sequences (i)-(vi) as shown in Fig. 2, which is also given by a flowchart (i) Analysis of past weather data in each time duration T : temperature v : windspeed p :rainfall x,w : weights y :insolation
(iii) Construction of NN for each cluster Tt ' 1
x11
vt '1
Q1
pt '1
Q2
Tt ' pt ' Tt ' 1 vt ' 1
2.1 Day-ahead Energy Management Planning Module This module forecasts the amount of the next-day demand and RES (PV and wind turbine (WT)) outputs, and builds upon a start-stop UC plan for the existing generators, including the batteries. In the day-ahead forecasting unit, the predictions are conducted at the intervals of 30 minutes according to the UC planning policy. The obtained results are then incorporated into the day-ahead UC planning policy. In the following section, a new method that generates and uses a
Qj
yˆ t '
Distribution Learning Error
・Weather (Sunny, Cloudy, Rainy) ・Wind direction and speed, 4 categorized max speed [0〜2m/s] [3〜5m/s] [6〜9m/s] [over 10m/s] ・Temperature
(v) Use of proper NN based on the weather condition of target day
(vi) PV generation prediction
Prediction
時刻 Time
(ii) Weather Clustering (WC) of past data into 3 clusters.
Japan Meteorological Agency (Local Area Time Series Data)
w jk
xij
pt '1
Present t'
w11
Q3
vt ' ・ ・・
In this work, a supply-demand control simulator that is shown in Fig. 1 and that is referred to as the supply-demand manager (Sasaki et al., 2017) is developed. The supplydemand manager is assumed to manage a relatively small power system, ranging from a single building to the level of a remote island. The small power system would consist of PVs, diesel-driven generators, batteries and loads. The supplydemand manager has a 3-layer hierarchy of speed control that covers time operations ranging from seconds to several hours. Furthermore, the manager aims at achieving the minimum exchange of information among the layers, and each layer is equipped with a minimal data set to operate independently even when there is a failure in the data transfer. The prediction module consists of the day-ahead as well as realtime prediction modules. The former prediction is used for the start-stop planning phase of the diesel generator group, which is referred to as UC. The latter information is then used for dynamic economic load dispatch (DELD), which is referred to as DELD (Sasaki et al., 2016, 2017).
PV output [W]
2. SUPPLY-DEMAND MANAGER OPERATIONS
On the specific target day, the real-time energy management operation module performs a real-time prediction of the load demand and the RES outputs for up to 1 hour in advance within a 5-minute interval. The predictions are executed every 5 minutes and the results are used in the UC policy on the day and the DELD of the generators and storage batteries. In order to cope with rapid weather changes and unavoidable prediction errors, a stand-by operating reserve and a spinning reserve are also preserved at the same time. A real-time prediction method will now be proposed by utilizing the solar radiation correlation analysis (SRCA) for this module. 2.3 Real-time Frequency Control Module
ＰＶ発電量 Insolation
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2σ σ
Setting of CI
(iv) Performance evaluation of NN using testing data, and computation of standard deviations of prediction errors. Setting of Confidence Intervals (CIs) based on historical data analysis
Fig. 2. The proposed scheme of PV output prediction.
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as depicted in Fig. 3 in Section 4. The following subsections provide further explanations on each of the parts (i)-(vi).
163
PV output predictions are now to be performed by using most of the recent available measurements and the weather forecasts by JMA. When the predictions are obtained, 3-hour basis predictions are transformed into every 30-min predictions through a linear interpolation process. The predicted solar radiation yˆt ' that is obtained in the previous
3.1 Step (i): Pre-processing of the Input Data In Japan, JMA (Japan Meteorological Agency) provides a general weather forecast to the public on a 3-hour basis on its website. The forecasted real-time data that are provided every 3 hours are as follows: (1) temperature T, (2) maximum wind speed v, (3) probability of the precipitation, and (4) weather class (sunny, cloudy, rainy, and snowy). Furthermore, past data are also available on a 3-hour basis which is as follows: (1) temperature T, (2) maximum wind speed v, and (3) the precipitation p (=1, if observed, and =0, if not). In this paper, the maximum wind speed v m/s is classified into 4 levels as “0-2”, “3-5”, “6-9” and “over 10 m/s.”
sub-section is transformed into the PV generation amount Pˆt ' . In our proposed method, the power generation estimation method (Japan Industrial Standard website) is used as specified by Pˆt ' K / GS PAS yˆt ' where K denotes the
monthly comprehensive design factor; Gs denotes the solar radiation intensity (kW/m2) in the standard test condition, and PAS denotes a standard solar cell array output. The monthly comprehensive design factor K can be obtained from K K HD K PD K PA K PM IND K PT where KHD denotes the solar radiation yearly variation correction coefficient, KPD denotes the aging correction coefficient, KPA denotes the array circuit correction coefficient, KPM denotes the array load matching correction coefficient, ηIND denotes the power conditioner effective efficiency, and KPT denotes the temperature correction factor. These coefficients are determined based on the condition of the PV system that is installed at the rooftop of the Graduate school of Engineering, Hiroshima University.
3.2 Step (ii): Data Clustering According to Weather Condition The amount of solar radiation is highly dependent on the weather. However, the provided past data by JMA do not include the weather class. Therefore, to meet the Agency’s forecast, the past data are classified and divided into three clusters (namely, sunny, cloudy, rainy) using thresholds corresponding to the radiation levels. Subsequently, three types of weather-dedicated NNs are constructed associated with the data.
4. REAL-TIME PV FORECAST METHOD
3.3 Step (iii): Construction of Neural Networks Three weather-dedicated NNs are constructed on a 3-hour interval basis. Each class of the NN is trained by using the corresponding class of data. Specifically, if the Agency’s forecasted data of the target time is sunny, the predictions are made by the NNs that are trained using the sunny cluster data. The training data are then represented as follows: (1) weather class, (2) temperature T, (3) wind speed v (4 levels), (4) precipitation p, and the amount of the solar radiation that is adopted as the teacher data. Note that the input data (T, v, p) consists of 3 sets for t' – 1 (3 hours before), t' (target given time) and t' + 1 (3 hours after). In addition, the weight coefficients of the hidden layer NNs are determined by using the backpropagation (BP) training method. 3.4 Step (iv): Performance Evaluation and CI Setting After the NN training process is completed, the performance of the trained NN is evaluated by using the testing dataset. Subsequently, the distribution of the solar radiation predictions and the errors are obtained. CIs are then analyzed based on the prediction errors. The CIs represent upper and lower bounds on the reliable ranges to visually recognize the reliability of the prediction results. By using the obtained prediction error standard deviation σ of the testing data, the CI is set as the range of ±σ, ±2σ around the mean value of the prediction errors. The CIs are effectively to be used in the power system operational planning and the real-time operation preserving robust security as mentioned earlier (not discussed in detail due to space limitations, refer to Sasaki et al., 2016, 2017).
The real-time PV forecast is performed based on multiple PV areas predictions and correlation analysis among the individual areas. The following sub-sections will provide detailed explanations on our proposed the method as depicted on Fig. 3 (b), which provides a flowchart of the entire process for the real-time forecast methodology. 4.1 Pre-processing of the Input Data The same data that were used for the day-ahead forecast in Section 3 are now used for this case as well. Neural networks are also used here for constructing a real-time forecasting strategy. The data are provided every 3 hours and include T, v, p, and the weather class (sunny, cloudy, rainy, and snowy). For the real-time forecast, the PV data for each area having a 5-minutes interval are generated by using linear interpolation, as described in the next step. 4.2 Correlation Analysis Similar to earlier strategy SRCA approach which evaluates the correlations of the solar radiations between the target location and the other selected locations based on their distances are proposed. SRCA is then implemented and applied for real-time PV power generation prediction scheme. The main idea behind the concept of SRCA can be described with respect to two specified locations A and B. It should be noted that solar radiation varies with movement of the clouds and there is a similarity in variation patterns among them having certain time delays. This phenomenon is due to cloud movement and the characteristics can be used for accomplishing the prediction objectives and goals of realtime forecast. The pattern variations corresponding to an upstream location are assumed to appear with a lag time at
3.5 Steps (v)-(vi): PV Generation Estimation 163
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the downstream location. The solar radiation of the upstream location where the correlation is judged to be high by the SRCA is then applied to the prediction of the downstream target location.
Yˆi (t )
i
i
j
j
xi (t ) xi x j (t ) x j 2
t
2
ij
(t )
(2)
r ( ) ij
i
denotes the predicted solar radiation at the target location i using all the screened locations at time t (kW/m2). 4.3 Real-time PV Forecasts for Individual Areas
data xi(t) by using linear interpolation scheme in the preprocessing module in Fig. 3. Next, according to equation (1), the solar radiation correlation coefficient rij between the target location i and the other location j is evaluated every 5 minutes, as given by t
ij
where yˆij (t ) denotes the predicted solar radiation at the target location i using the location j at time t (kW/m2) and Yˆ (t )
outputs of the NNs corresponding to the location I, where a 3-hour duration data yˆ t ' are transformed into a 5-minute basis
x (t ) x x (t ) x
j
j
The gap times where similar solar radiation fluctuations appear are derived by using the SRCA, and the location candidates are selected based on their values. First, the solar radiation data, xi(t) (kW/m2) are generated from yˆ t ' , i.e., the
rij ( )
r ( ) yˆ
(1)
t
where t denotes time (measured in minutes), τ denotes the gap time (min), x denotes the average of x within a period of time (kW/m2). Once the correlation coefficients are calculated using (1), the correlation gap time τ is obtained. The gap times for the other locations are adopted for the solar radiation prediction of the target location i. In other words, the solar radiation data of τ minutes earlier is then used for prediction of the target location. Moreover, to obtain a stable and an accurate prediction result, the above procedure is conducted and performed for all the candidates in real-time, and the analysis data are updated every 5 minutes. The correlation coefficient rij is regarded as the weight factor and the weighted average of the solar radiation prediction can therefore be calculated by using equation (2) as follows:
The same methodology for constructing the NNs as described in Section 3 for the day-ahead PV forecasts is now applied to the real-time PV forecast for individual areas. The difference is in only the data that are utilized. That is, all the data provided every 3 hours by the JMA are transformed into 5minute interval data in the pre-processing section of Fig. 3. The data for the individual locations are then used to construct the NNs, which are used for real-time prediction of the target areas and the other locations. The same procedure as given in steps (ii) to (vi) of Fig. 3 are now performed by using the 5-minute interval data to compute yˆij (t ) in (4). Consequently, the estimation of the real-time PV outputs is obtained by using equation (2). 5. NUMERICAL CASE STUDIES In this section, based on the procedure that was provided in Fig. 3, the performance of our proposed methodology is demonstrated and illustrated through the following two case studies as described below: 1) Local prediction for the PV system at the Hiroshima University (40 kW) using the historical data of the PV output combined with the weather data that are provided by JMA. 2) Global PV generation prediction by using the solar radiation meter data that are provided by the Ministry of Economy, Trade and Industry. For the first case, the day-ahead PV power generation prediction as well as real-time prediction is obtained to show their relative performance and effectiveness. An autoregressive (AR) model is used as a conventional method for performing comparisons. For the second case we compare the accuracy and performance of the proposed prediction scheme with the existing approaches in the literature. 5.1 Day-ahead PV Generation Forecast Results Figures 4 and 5 show the forecast results of a typical day when the PV generation fluctuates significantly. The vertical axis denotes a 30-minute integrated value of the PV power generation. The blue line vertically extending from the predicted value shows the range of ±σ and the red line denotes the range of ± 2σ. The weather marks, which appear at the bottom of the figure, show the corresponding time weather of 3, 6, 9, 12, 15, 18, and 21 from the left (sunny, cloudy, or rainy). When the weather is changing slowly, both results are confirmed to be relatively accurately predicted. For the CI, the measured values are basically within the desired CIs.
Fig. 3. Flowchart of our proposed PV prediction method. 164
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Table I. Prediction errors in June 2012. Proposed method AR method
Av. Error (%) Max Error (%) Av. Error (%) Max Error (%)
The prediction error rate 𝜀𝜀 and the maximum prediction error rate η, as expressed in equations (4) and (5) below, are used as indices for comparing and evaluating the prediction accuracy. The amount of global solar radiation is the theoretical value that represents the ideal maximum amount of solar radiation of the day. Table I shows the average and maximum prediction errors for each target time in June 2012.
Fig. 4. PV forecasting with weather clustering.
Figures 7 and 8 provide the predicted and the actual values of the 60-minutes and the 10-minutes ahead forecasts, respectively. The maximum expected error rate η in the autoregressive (AR) model has greatly been increased over the 5 minutes to the 10 minutes periods. Consequently, the prediction accuracy of the proposed method is almost the same as that of the AR model.
Fig. 5. PV forecasting without weather clustering.
100
When the power generation output is small in the morning or in the evening, the CI variance is small whereas the CI has a large variance when the fluctuations become large during the daytime. It can be confirmed that the CI variance is smaller when the weather classification method is applied.
2
2
(4)
100 X max Xˆ max Qmax [%]
(5)
Moreover, the prediction accuracy is verified for all seasons under different weather conditions by using the data from October-December in 2011 and January-September in 2012 due to their availability. Figure 9 shows the monthly forecast error rate of the 30-minutes ahead forecast. In our proposed method, it is possible to predict in advance fluctuations of the solar radiation to some extent, such as the lamp fluctuations. It can be concluded that our proposed method is capable of suppressing the average error (Av. Error), the maximum error (Max. Error), and MAE for each season compared to the AR model.
Figure 6 shows the transition of the average prediction error and the maximum prediction error for each month. The prediction error is calculated by utilizing the equation (3) as given below (root mean square error: RMSE) where N denotes the data number, y denotes the measured value, and yˆ denotes the predicted value, as follows:
where Xˆ denotes the solar radiation predicted value, Xˆ max denotes the solar radiation predicted value that causes the maximum prediction error, X denotes the solar radiation actual value, Xmax denotes the solar radiation actual value that causes the maximum prediction error, Q denotes the extraterrestrial solar radiation, and Qmax denotes the maximum value of the extra-terrestrial solar radiation.
Fig. 6. RMSE and maximum errors in 2012.
1 N Ps (t ) Pˆs (t ) N t 1
1 m ˆ Xl Xl m l 1 1 m Q m l 1
[%]
RMSE
5 min 10 min 20 min 40 min 60 min ahead ahead ahead ahead ahead 16.09 19.00 20.75 23.76 25.07 16.50 18.47 19.84 21.20 22.26 32.87 36.74 36.56 39.10 40.65 31.43 32.48 33.91 34.89 35.12
(3)
5.3 Comparison of Prediction Performance Performance of our proposed prediction method is now compared with the existing methods in the literature (Yamamoto et al., 1999, Simose et al., 2014) using the solar radiation meter data of the Chugoku region that are provided by the Ministry of Economy, Trade and Industry recorded in the subsidized project “distributed new energy mass introduction promotion system stabilization measures
When the weather classification is performed the average error in most months is shown to be reduced. However, for the maximum error, the increased errors were observed in certain months. In the classification problem, the weather is assumed to be the same for 3 hours and the method could not follow rapid weather changes within 3 hours. 5.2 Real-time PV Generation Forecast Results 165
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6. CONCLUSIONS This paper has proposed a day-ahead as well as a real-time PV generation forecasting methodology for the supplydemand manager module. The performance, capabilities, and effectiveness of our proposed methodology have been verified through extensive simulation case studies. Furthermore, prediction error degree of influence and the CI on the system performance have been validated. There are still a number of research issues that have to be developed for the day-ahead forecast to improve the prediction accuracy, including using a validation data set to examine and overcome the potential over-fitting problem in NN training. Moreover, implementation of our scheme on a simulator to study how significant the impact on prediction errors and/or the CI settings will influence the results should be investigated. For achieving a real-time forecast, it is necessary to take into consideration the relationship between the additional weather conditions beside the solar radiation characteristics. Additionally, effective settings for the CI based on the prediction error analysis and construction of a detection system for performing short-time lamp fluctuations in advance also remain as topics for our future research work.
actual Measurement CA model Proposed ARmodel AR
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Fig. 7. 60-min ahead predictions.
0.6 0.4 0.2 0 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00
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REFERENCES
Fig. 8. 10-min ahead predictions. ε (Proposed) (CA)
ε (AR)
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Asrari, A., T.X. Wu, and B. Ramos, “A Hybrid Algorithm for Short-Term Solar Power Prediction – Sunshine State Case Study,” IEEE Trans. Sustainable Energy, Vol. 8, No. 2, pp.582-591, Apr. 2017. Bessa, R.J., A. Trindade, and V. Miranda, “Spatial-Temporal Solar Power Forecasting for Smart Grids,” IEEE Trans. Industrial Informatics, Vol. 11, Vo. 1, pp.232-241, Feb. 2015 Hafiz, H.M., N. Yorino, Y. Sasaki, and Y. Zoka, “Feasible Operation Region for Dynamic Economic Dispatch and Reserve Monitoring,” European Trans. on Electrical Power, Vol. 22, No. 7, pp.924-936, 2012. Japan Meteorological Agency: http://www.jma.go.jp/jma/indexe.html Japan Industrial Standard (JIS) C8907, “Estimation method of generating electric energy by PV power system,” 2005. Matsuda, K., K. Arimatsu, K. Yamase, M. Watanabe, J. Yamazaki, and J. Murakoshi, “Short-term Prediction of Photovoltaic Power Generation for Distribution Management System based on Spatial Correlation Analysis,” IEEJ Trans. on Power and Energy, Vol.134, No.9, pp.759-766, Sep., 2014. Ministry of Economy, Trade and Industry (METI), http://www.meti.go.jp/english/ Sasaki, Y., N. Yorino, and Y. Zoka, “Probabilistic Economic Load Dispatch Applied to a micro-EMS Controller,” 19th Power Systems Computation Conference, No. ID574, Jul. 2016. Sasaki, Y., N. Yorino, Y. Zoka, and I.F. Wahyudi, “Robust Stochastic Dynamic Load Dispatch against Uncertainties,” IEEE Trans. on Smart Grid, Vol. PP, No. 99, pp.1-9, 2017 (to appear). Simose, K., H. Ohtake, J.G da Silva Fonseca Jr., T. Takashima, T. Oozeki, Y. Yamada, “Analysis of Error Causes of the Irradiation Forecast by the Japan Meteorological Agency Meso-Scale Model,” IEEJ Trans. on Power and Energy, Vol.134, No.6, pp.518-526, Jun., 2014. Yamada H. and O. Ikki, “National Survey Report of PV Power Applications in JAPAN,” International Energy Agency Photovoltaic Power Systems Programme, issue date: 22 Aug. 2016. Yamamoto, S., T. Katagi, J. Park, and T. Hashimoto, and T. Hashimoto “A Basic Study to Forecast the Power Fluctuation of the Photovoltaic Power Generation by Image Processing of Clouds,” IEEJ Trans. on Power and Energy, Vol.119-B, No.8/9, pp.909-915, Aug./Sep., 1999. Yang, H.T., C.M. Huang, Y.C. Huand, and Y.S. Pai, “A Weather-based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output,” IEEE Trans. Sustainable Energy, Vol. 5, No. 3, pp.917-926, Jul. 2014. Yorino, N., H.M. Hafiz, Y. Sasaki, and Y. Zoka, “High-speed Real-time Dynamic Economic Load Dispatch,” IEEE Trans. on Power Syst., Vol. 27, No. 2, pp.621-630, May 2012. Zhang, Y., M. Beaudin, R. Taheri, and H. Zareipour, “Day-Ahead Power Output Forecasting for Small-Scale Solar Photovoltaic Electricity Generators,” IEEE Trans. Smart Grid, Vol. 6, No. 5, pp.2253-2262, Sep. 2015.
0
Fig. 9. Prediction errors. business” (Ministry of Economy, Trade and Industry, website), and the past weather data by the JMA. A recent study (Yamamoto et al., 1999) has proposed to use cloud images that are observed from ground to improve the prediction accuracy where the MAE of 100-120 (Wh/(m2•h)) is obtained (maximum error is not reported). On the other hand, our method realizes RMSE of 55-80 (Wh/(m2•h)) and MAE of 70-110 (Wh/(m2•h)) as shown in Fig. 6. Another research (Simose et al., 2014) utilizes the cloud images that are obtained from weather satellites to obtain improved predictions where it is reported that the maximum error of 24.5 (%) is obtained (but MAE was not provided). However, our proposed method provides an equivalent performance of the maximum error of 200 (Wh/(m2•h)) and the maximum error rate of 24.8 (%), as provided in Fig. 6. The above conclusions and observations allow one to conclude that in spite of using no complex meteorological imagery and satellites data, our proposed method provides accurate prediction by using only very general data that are commonly available even in disadvantaged areas. 166
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A A Simple Simple and and Reliable Reliable Photovoltaic Photovoltaic Forecast Forecast for for Reliable Reliable Power Power System System Operation Operation A Simple and Reliable Photovoltaic Forecast for Reliable Power System Operation Control Control Control
L. Ma **, N. Yorino *, Y. Sasaki *, Y. Zoka *, K. Khorasani **, A.B. Rehiara * L. Ma **, N. Yorino *, Y. Sasaki *, Y. Zoka *, K. Khorasani **, A.B. Rehiara * L. **, *, Y. Sasaki Khorasani **, A.B. Zoka L. *Ma Ma **, N. N. Yorino Yorino Sasaki *, *, Y. Y. Zoka *, *, K. K. Khorasani **,Kagamiyama, A.B. Rehiara Rehiara ** Graduate School *, of Y. Engineering, Hiroshima University, 1-4-1 * Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, 739-8527 JapanHiroshima (Contact email: [email protected]) Graduate School School of of Engineering, University, 1-4-1 Kagamiyama, Kagamiyama, Higashi-Hiroshima, 739-8527 JapanHiroshima (Contact email: [email protected]) ** Graduate Engineering, University, 1-4-1 ** Department of Electrical and Computer Engineering, Concordia University, Higashi-Hiroshima, 739-8527and Japan (Contact email: [email protected]) [email protected]) ** Department of Electrical Computer Engineering, Concordia University, Higashi-Hiroshima, 739-8527 Japan (Contact email: QC, 1M8 Canada (Contact email: [email protected]) **Montreal, Department ofH3G Electrical and Computer Computer Engineering, Concordia University, University, Montreal, QC, H3G 1M8 Canada (Contact email: [email protected]) ** Department of Electrical and Engineering, Concordia Montreal, QC, H3G 1M8 Canada (Contact email: [email protected]) Montreal, QC, H3G 1M8 Canada (Contact email: [email protected]) Abstract: Recently various forecasting methods for photovoltaic (PV) generation have been proposed in Abstract: Recently various forecasting methods for photovoltaic (PV) generation have been proposed in the literature. However, these standard methods cannot be successfully and widely used been in general due to Abstract: Recently various methods for (PV) proposed the literature. However, theseforecasting standard methods be successfully and widely have used been in general due in to Abstract: Recently various forecasting methodscannot for photovoltaic photovoltaic (PV) generation generation have proposed in the fact that they require access to specialized data that are not always and everywhere readily available the However, standard methods cannot used general due fact that they requirethese access to specialized thatbe aresuccessfully not always and and widely everywhere readily available the literature. literature. However, these standard methodsdata cannot be successfully and widely used in in general due to to in practice. Furthermore, prediction accuracy of such methods tends to deteriorate specially due to data the fact they to data that are always everywhere readily available in prediction accuracy of such deteriorate specially due to data thepractice. fact that that Furthermore, they require require access access to specialized specialized data thatmethods are not not tends alwaystoand and everywhere readily available scarcity. This paper proposes a simple and reliable PV forecasting method using machine learning and in Furthermore, prediction accuracy of tends to specially due scarcity. This paper proposes a simple and reliable forecasting using machine and in practice. practice. Furthermore, prediction accuracy of such suchPVmethods methods tendsmethod to deteriorate deteriorate speciallylearning due to to data data neural networks. Confidence interval (CI) results are specifically provided for the local supply-demand scarcity. This paper paper proposes interval simple(CI) andresults reliable PV forecastingprovided method for using machine learning and and neural networks. Confidence arePV specifically themachine local supply-demand scarcity. This proposes aa simple and reliable forecasting method using learning controlnetworks. as well as for the robust power systemare security. The provided proposed for method usessupply-demand only weather neural Confidence intervalpower (CI) results results specifically the local local control as well as for the robust systemare security. The provided proposed for method usessupply-demand only weather neural networks. Confidence interval (CI) specifically the forecasting data that are the provided bypower the Japan Meteorological Agency (JMA) and which isonly available to control as well well as for for robustby system security. The The proposed method uses is weather forecasting data that are the provided the Japan Meteorological Agency (JMA) and which available to control as as robust power system security. proposed method uses only weather the public. The proposed method maintains a high level of accuracy by using real-time correlation data forecasting data proposed that are are provided provided by the the Japan Japan Meteorological Agency (JMA) and which whichcorrelation is available available to the public. The method maintains a high level of accuracy by(JMA) using real-time data forecasting data that by Meteorological Agency and is to between theThe specific target and themaintains neighboring areas. Multiple neural networks are constructed based on the proposed aa high level of using correlation between theThe specific targetmethod and themaintains neighboring areas. Multiple neural by networks are constructed baseddata on the public. public. proposed method high level of accuracy accuracy by using real-time real-time correlation data a weathertheclustering technique. It has been confirmed through extensive simulation results that the between specific and areas. neural networks are on abetween weathertheclustering technique. has been confirmed through extensive simulation results based that the specific target target and the theIt neighboring neighboring areas. Multiple Multiple neural networks are constructed constructed based on proposed method demonstrates robustness in prediction accuracy and CI effectiveness. aproposed weather clustering technique. It has been confirmed through extensive simulation results that the demonstrates in prediction accuracy CI effectiveness. a weathermethod clustering technique.robustness It has been confirmed throughand extensive simulation results that the proposed method demonstrates robustness in prediction accuracy and effectiveness. © 2018, IFAC (International Federation of Automatic Control) Hosting byCI Ltd. All rights reserved. proposed method demonstrates robustness inenergy prediction accuracy andNeural CIElsevier effectiveness. Keywords: Confidence intervals, Local management, networks, PV forecasting, Keywords: Confidence intervals, Local energy management, Neural networks, PV forecasting, Uncertainties. Keywords: Confidence Confidence intervals, intervals, Local Local energy energy management, management, Neural Neural networks, networks, PV PV forecasting, forecasting, Uncertainties. Keywords: Uncertainties. Uncertainties. real-time operation and control of a small size power system, real-time operation and control of a small size power system, 1. INTRODUCTION which canoperation representand a given single building to the level of a 1. INTRODUCTION real-time control of small system, which canoperation representand a given single the level of a real-time control of aa building small size sizetopower power system, 1. INTRODUCTION remote island power network. 1. INTRODUCTION Renewable energy sources (RESs) which are not based on which represent aa given island power network. which can can represent given single single building building to to the the level level of of aa Renewable energy sources (RESs) which are not based on remote fossil fuel have attracted significant attention due to their remote island power network. power network. Renewable energy sources (RESs) are on Variousisland PV prediction methods are available in the literature fossil fuel have attracted attention duebased to their Renewable energy sourcessignificant (RESs) which which are not not based on remote Various PV prediction methods are available in the literature reduced environmental impact. Inattention particular, active fossil have due their as the day-ahead forecasting for a small-scale PV reduced impact. Inattention particular, fossil fuel fuel environmental have attracted attracted significant significant due to toactive their such are in such as PV theprediction day-aheadmethods forecasting for a small-scale PV installation of photovoltaic impact. (PV) power generation is being Various Various PV prediction methods are available available in the the literature literature reduced In particular, et al., 2015), the spatial-temporal solar short-term installation of photovoltaic impact. (PV) power being (Zhang reduced environmental environmental In generation particular, is active active such as as etthe the day-ahead forecasting for aa solar small-scale PV (Zhang al., 2015), the spatial-temporal short-term strongly promoted in Japan (Yamada, 2016). However, the such day-ahead forecasting for small-scale PV installation of power generation is (Bessa et al., and hybridsolar algorithms for strongly promoted in Japan (PV) (Yamada, However, the forecasting installation of photovoltaic photovoltaic (PV) power2016). generation is being being (Zhang et et al., al., 2015), the 2015), spatial-temporal short-term forecasting (Bessa et al., 2015), and hybrid algorithms for PV output is generally uncertain due to spatial and time(Zhang 2015), the spatial-temporal solar short-term strongly promoted in (Yamada, However, the PV prediction (Asrari et al., 2017,algorithms Yang, et al., PV output is generally uncertain due 2016). to spatial and timestrongly promoted in Japan Japan (Yamada, 2016). However, the short-term forecasting (Bessa et al., 2015), and hybrid for short-term PV prediction (Asrari et al., 2017, Yang, et al., varying changes in solar radiation. Therefore, by using forecasting (Bessa et al., 2015), and hybrid algorithms for PV is uncertain due to time2014), among others. The worket al., Matsuda et al., et2014 varying changes in solar radiation. by using PV output output is generally generally uncertain due Therefore, to spatial spatial and and time- short-term PV prediction (Asrari 2017, Yang, al., 2014), among others. The work Matsuda et al., 2014 controllable generator groups that correspond to existing short-term PV prediction (Asrari et al., 2017, Yang, et al., varying in radiation. Therefore, using a PV others. prediction method for localet distribution controllable generator groups that correspond varying changes changes in solar solar radiation. Therefore,toby byexisting using proposes 2014), The work al., a PV others. prediction for localet distribution thermal power and/or diesel generators, it would to be possible 2014), among among The method work Matsuda Matsuda al., 2014 2014 controllable generator groups that correspond correspond existing proposes control system based on the correlation analysis of the thermal power and/or diesel generators, it would to be possible controllable generator groups that existing proposes a PV prediction method for local distribution control system based on the correlation analysis of the to achieve a stable power supply based on well-suited proposes a PV prediction method for local distribution thermal power and/or diesel generators, it would would be possible observed data. Other methods use cloud images observed to achieve a and/or stable diesel powergenerators, supply based on be well-suited thermal power it possible control system based on the correlation analysis of the observed data. Other methods use cloud images observed generation planning, operation and control policies. control system based on the correlation analysis of the to aa stable power based on the ground (Yamamoto et al., 1999) orimages that are observed obtained generation and control policies. to achieve achieveplanning, stableoperation power supply supply based on well-suited well-suited from observed data. Other methods use cloud from the ground (Yamamoto et al., 1999) or that are obtained observed data. Other methods use cloud images observed generation planning, control weather satellites (Simose et al., 2014). However, these generation planning, operation and control policies. On the other hand, ifoperation one can and readily and policies. accurately predict from ground (Yamamoto et 1999) or are weather satellites (Simose et al., 2014). However, these from the the ground (Yamamoto et al., al., 1999) or that that are obtained obtained On the other hand, if one can readily and accurately predict from complex and time-consuming methods require access and the amount of solar radiation it would be possible to then from weather satellites (Simose et al., 2014). However, these complex and time-consuming methods require access and from weather satellites (Simose et al., 2014). However, these On the other hand, if one can readily and accurately predict the amount solarif radiation it would possible to then availability of special data such as the meteorological image On the otherofhand, one can readily andbeaccurately predict make efficient operational plans for both electric power complex and time-consuming methods require access and availability of special data such as the meteorological image complex and time-consuming methods require access and the amount of solar radiation it would be possible to then make efficient operational plans for both electric power the amount of solar radiation it would be possible to then data, solar data from the radiation meters at power grid storages as well as thermal and hydraulic generators. In availability of special data such as the meteorological image solar of data fromdata the such radiation at powerimage grid availability special as themeters meteorological make efficient efficient operational plans for both electric electric power storages as welloperational as thermalplans and for hydraulic generators. In data, make both power substations, among others that are not readily available in addition, by well analyzing the prediction error andgenerators. its statistical solar from the radiation meters at power substations, among others are not readily in data, solar data data from the that radiation meters at available power grid grid storages as and In addition, by well analyzing the prediction error andgenerators. its statistical storages as as as thermal thermal and hydraulic hydraulic In data, practice. Furthermore, these methods focus their attention on distribution, it would bethe possible to maintain a proper reserve substations, among are readily in Furthermore, thesethat methods focus their available attention on substations, among others others that are not not readily available in addition, by prediction error its distribution, would bethe possible to maintain a proper reserve practice. addition, by itanalyzing analyzing prediction error and and its statistical statistical the accuracy of obtained predictions and there areattention practically margin for a stable supply-demand control policy. practice. Furthermore, these methods focus their on the accuracy of obtained predictions and there are practically practice. Furthermore, these methods focus their attention on distribution, be to aa proper margin for it a stable supply-demand policy. no examination conducted on the CI metric. distribution, it would would be possible possible to maintain maintain control proper reserve reserve Consequently, in addition to developingcontrol an accurate accuracy obtained and no on the CI metric. theexamination accuracy of of conducted obtained predictions predictions and there there are are practically practically margin supply-demand policy. Consequently, addition to developingcontrol an accurate margin for for aa in stable stable supply-demand policy. the prediction technique, development of advanced energy no examination conducted on the CI metric. no examination conducted on the CI metric. Consequently, in addition to developing an accurate prediction technique, development of advanced energy This paper proposes a PV forecasting methodology by Consequently, in addition to developing an accurate This paper proposes a PV forecasting methodology by management technology is regarded as an important prediction development of advanced energy neural networks (NNs) that are trained based onbya management technology is regarded an important prediction technique, technique, development of as advanced energy utilizing This paper paper proposes PV forecasting forecasting methodology utilizing neural networks that are trained based onbya objective and goal to be accomplished by researchers. This proposes aa (NNs) PV methodology management technology is as amount of meteorological information thatbased are readily objective and goal to be accomplished by researchers. management technology is regarded regarded as an an important important limited utilizing neural networks (NNs) that are trained on limited amount of meteorological information that are readily utilizing neural networks (NNs) that are trained based on aa objective and goal to be accomplished by researchers. available from local areas. The proposed method can predict objective and goal to be accomplished by researchers. In this paper, a “supply-demand manager” module as well as limited amount of meteorological information that are readily from local areas. The proposed method predict limited amount of meteorological information that can are readily In this paper, a “supply-demand manager” module as well as available the day-ahead as well as real-time PV outputs and directly al.,well 2012, aInsimulator is aproposed as shownmanager” in Fig. 1 (Hafiz from local areas. The method can predict the day-ahead well as real-time PV outputs available from as local areas. The proposed proposed methodand can directly predict this paper, paper, “supply-demand moduleet as as available et as al.,well 2012, aInsimulator is aproposed as shownmanager” in Fig. 1 (Hafiz this “supply-demand module as evaluates the CIaserror that is useful PV for devising the directly supplyYorino et al., 2012, Sasaki et al., 2017). The supply-demand the day-ahead well as real-time outputs and evaluates the CI error that is useful for devising the supplyday-ahead as well as real-time PV outputs and directly (Hafiz et aa simulator is as Fig. Yorino et al., 2012, Sasaki et al., in 2017). supply-demand (Hafiz et al., al., 2012, 2012, the simulator is proposed proposed as shown shown in Fig. 11The control policies. manageretmodule is Sasaki developed for2017). operational planning and demand the error demand evaluatescontrol the CI CIpolicies. error that that is is useful useful for for devising devising the the supplysupplyYorino al., et The manageretmodule is Sasaki developed for2017). operational planning and evaluates Yorino al., 2012, 2012, et al., al., The supply-demand supply-demand demand control policies. demand control policies. manager module is developed for operational planning and manager module is developed for operational planning and Copyright © 2018 IFAC 161 2405-8963 © 2018, IFAC (International Federation of Automatic Control) Copyright 2018 IFAC 161 Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. Copyright © © 2018 2018 IFAC IFAC 161 Copyright 161 10.1016/j.ifacol.2018.11.695
L. Ma et al. / IFAC PapersOnLine 51-28 (2018) 161–166
To summarize, our main contributions are stated as follows: (1) The accuracy of our proposed PV prediction scheme is shown to be sufficiently and acceptably high despite the fact that the method uses only a quite general set of data that are commonly available even in disadvantaged areas, and (2) Specific CI results are explicitly provided for use in local supply-demand control policy to realize a robust power system security. The second contribution above is realized by combination and integration of the following new techniques as described in detail subsequently. Specifically, based on the classification of weather forecast, a cluster of NNs are constructed that are dedicated to the individual forecasted weathers to achieve a high and accurate prediction performance. The correlation between the target area and the neighboring regions is also utilized. A solar radiation correlation analysis (SRCA) technique is then proposed and applied to the problem of real-time prediction. Specifically, the concept of “time distance” is introduced to take into account spatial-temporal elements with NNs. Effectiveness and accuracy of our proposed method is then demonstrated by using the weather data that are provided by the Japan Meteorological Agency (JMA), and the solar radiation meter data that are provided by the Ministry of Economy, Trade and Industry (Ministry of Economy, Trade and Industry, website).
Online Database
Offline Database Past Electricity Demand, Weather Information, Specification of Generator (Gen.) and Storage Battery (BT), Network Information, etc.
WT
Real-time Demand and Weather Information, Gen. and BT Operation Information, Network Operation Information, etc.
BT
PV Day ~ Hour order Day-ahead Planning Manager
Hour ~ Min. order Real-time Operation Manager
Day-ahead Forecast
Min. ~ Sec. order Real-time Control Manager
Real-time Forecast
・Electricity Demand ・Photovoltaic (PV) Gen. ・Wind-turbine (WT) Gen. > Prediction Errors > Confidence Intervals (CIs)
Normal State Control
・Electricity Demand ・PV, WT > Covariance matrices > CIs
・Load Frequency Control (LFC) ・Area requirement evaluation
Gen. Dispatching
・Gen. shedding ・Load control (shedding)
Gen. Scheduling
〜
Customer
Governor
Emergency Control
・Stochastic dynamic economic load dispatch using robust time-sequence dynamic feasible region (RTDF) > Real-time Gen. schedule > BT re-scheduling > Supply and Demand Mismatch (SDM)
・Unit Commitment (UC) > Gen. Start/Stop schedule > BT operation patterns > Reserve Management
〜
Customer
Governor
Re-dispatching
・Gen./BT re-dispatching
〜
Customer
Governor
Information Board > = <
SDM
Fig. 1. Proposed supply & demand management module. cluster of weather-dedicated NNs for each target time is proposed and developed. 2.2 Real-time Energy Management Operation Module
On the specific target day, a real-time frequency control is performed and performance including prediction results, operational planning and real-time operation are all evaluated by simulations associated with the frequency variations. 3. DAY-AHEAD PV FORECAST METHOD Figure 2 depicts the general methodology of our proposed day-ahead PV generation forecast scheme which provides a 24-hour PV prediction having a 30-minute resolution. The forecasting process is conducted in the following sequences (i)-(vi) as shown in Fig. 2, which is also given by a flowchart (i) Analysis of past weather data in each time duration T : temperature v : windspeed p :rainfall x,w : weights y :insolation
(iii) Construction of NN for each cluster Tt ' 1
x11
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2.1 Day-ahead Energy Management Planning Module This module forecasts the amount of the next-day demand and RES (PV and wind turbine (WT)) outputs, and builds upon a start-stop UC plan for the existing generators, including the batteries. In the day-ahead forecasting unit, the predictions are conducted at the intervals of 30 minutes according to the UC planning policy. The obtained results are then incorporated into the day-ahead UC planning policy. In the following section, a new method that generates and uses a
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(v) Use of proper NN based on the weather condition of target day
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In this work, a supply-demand control simulator that is shown in Fig. 1 and that is referred to as the supply-demand manager (Sasaki et al., 2017) is developed. The supplydemand manager is assumed to manage a relatively small power system, ranging from a single building to the level of a remote island. The small power system would consist of PVs, diesel-driven generators, batteries and loads. The supplydemand manager has a 3-layer hierarchy of speed control that covers time operations ranging from seconds to several hours. Furthermore, the manager aims at achieving the minimum exchange of information among the layers, and each layer is equipped with a minimal data set to operate independently even when there is a failure in the data transfer. The prediction module consists of the day-ahead as well as realtime prediction modules. The former prediction is used for the start-stop planning phase of the diesel generator group, which is referred to as UC. The latter information is then used for dynamic economic load dispatch (DELD), which is referred to as DELD (Sasaki et al., 2016, 2017).
PV output [W]
2. SUPPLY-DEMAND MANAGER OPERATIONS
On the specific target day, the real-time energy management operation module performs a real-time prediction of the load demand and the RES outputs for up to 1 hour in advance within a 5-minute interval. The predictions are executed every 5 minutes and the results are used in the UC policy on the day and the DELD of the generators and storage batteries. In order to cope with rapid weather changes and unavoidable prediction errors, a stand-by operating reserve and a spinning reserve are also preserved at the same time. A real-time prediction method will now be proposed by utilizing the solar radiation correlation analysis (SRCA) for this module. 2.3 Real-time Frequency Control Module
ＰＶ発電量 Insolation
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2σ σ
Setting of CI
(iv) Performance evaluation of NN using testing data, and computation of standard deviations of prediction errors. Setting of Confidence Intervals (CIs) based on historical data analysis
Fig. 2. The proposed scheme of PV output prediction.
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as depicted in Fig. 3 in Section 4. The following subsections provide further explanations on each of the parts (i)-(vi).
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PV output predictions are now to be performed by using most of the recent available measurements and the weather forecasts by JMA. When the predictions are obtained, 3-hour basis predictions are transformed into every 30-min predictions through a linear interpolation process. The predicted solar radiation yˆt ' that is obtained in the previous
3.1 Step (i): Pre-processing of the Input Data In Japan, JMA (Japan Meteorological Agency) provides a general weather forecast to the public on a 3-hour basis on its website. The forecasted real-time data that are provided every 3 hours are as follows: (1) temperature T, (2) maximum wind speed v, (3) probability of the precipitation, and (4) weather class (sunny, cloudy, rainy, and snowy). Furthermore, past data are also available on a 3-hour basis which is as follows: (1) temperature T, (2) maximum wind speed v, and (3) the precipitation p (=1, if observed, and =0, if not). In this paper, the maximum wind speed v m/s is classified into 4 levels as “0-2”, “3-5”, “6-9” and “over 10 m/s.”
sub-section is transformed into the PV generation amount Pˆt ' . In our proposed method, the power generation estimation method (Japan Industrial Standard website) is used as specified by Pˆt ' K / GS PAS yˆt ' where K denotes the
monthly comprehensive design factor; Gs denotes the solar radiation intensity (kW/m2) in the standard test condition, and PAS denotes a standard solar cell array output. The monthly comprehensive design factor K can be obtained from K K HD K PD K PA K PM IND K PT where KHD denotes the solar radiation yearly variation correction coefficient, KPD denotes the aging correction coefficient, KPA denotes the array circuit correction coefficient, KPM denotes the array load matching correction coefficient, ηIND denotes the power conditioner effective efficiency, and KPT denotes the temperature correction factor. These coefficients are determined based on the condition of the PV system that is installed at the rooftop of the Graduate school of Engineering, Hiroshima University.
3.2 Step (ii): Data Clustering According to Weather Condition The amount of solar radiation is highly dependent on the weather. However, the provided past data by JMA do not include the weather class. Therefore, to meet the Agency’s forecast, the past data are classified and divided into three clusters (namely, sunny, cloudy, rainy) using thresholds corresponding to the radiation levels. Subsequently, three types of weather-dedicated NNs are constructed associated with the data.
4. REAL-TIME PV FORECAST METHOD
3.3 Step (iii): Construction of Neural Networks Three weather-dedicated NNs are constructed on a 3-hour interval basis. Each class of the NN is trained by using the corresponding class of data. Specifically, if the Agency’s forecasted data of the target time is sunny, the predictions are made by the NNs that are trained using the sunny cluster data. The training data are then represented as follows: (1) weather class, (2) temperature T, (3) wind speed v (4 levels), (4) precipitation p, and the amount of the solar radiation that is adopted as the teacher data. Note that the input data (T, v, p) consists of 3 sets for t' – 1 (3 hours before), t' (target given time) and t' + 1 (3 hours after). In addition, the weight coefficients of the hidden layer NNs are determined by using the backpropagation (BP) training method. 3.4 Step (iv): Performance Evaluation and CI Setting After the NN training process is completed, the performance of the trained NN is evaluated by using the testing dataset. Subsequently, the distribution of the solar radiation predictions and the errors are obtained. CIs are then analyzed based on the prediction errors. The CIs represent upper and lower bounds on the reliable ranges to visually recognize the reliability of the prediction results. By using the obtained prediction error standard deviation σ of the testing data, the CI is set as the range of ±σ, ±2σ around the mean value of the prediction errors. The CIs are effectively to be used in the power system operational planning and the real-time operation preserving robust security as mentioned earlier (not discussed in detail due to space limitations, refer to Sasaki et al., 2016, 2017).
The real-time PV forecast is performed based on multiple PV areas predictions and correlation analysis among the individual areas. The following sub-sections will provide detailed explanations on our proposed the method as depicted on Fig. 3 (b), which provides a flowchart of the entire process for the real-time forecast methodology. 4.1 Pre-processing of the Input Data The same data that were used for the day-ahead forecast in Section 3 are now used for this case as well. Neural networks are also used here for constructing a real-time forecasting strategy. The data are provided every 3 hours and include T, v, p, and the weather class (sunny, cloudy, rainy, and snowy). For the real-time forecast, the PV data for each area having a 5-minutes interval are generated by using linear interpolation, as described in the next step. 4.2 Correlation Analysis Similar to earlier strategy SRCA approach which evaluates the correlations of the solar radiations between the target location and the other selected locations based on their distances are proposed. SRCA is then implemented and applied for real-time PV power generation prediction scheme. The main idea behind the concept of SRCA can be described with respect to two specified locations A and B. It should be noted that solar radiation varies with movement of the clouds and there is a similarity in variation patterns among them having certain time delays. This phenomenon is due to cloud movement and the characteristics can be used for accomplishing the prediction objectives and goals of realtime forecast. The pattern variations corresponding to an upstream location are assumed to appear with a lag time at
3.5 Steps (v)-(vi): PV Generation Estimation 163
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the downstream location. The solar radiation of the upstream location where the correlation is judged to be high by the SRCA is then applied to the prediction of the downstream target location.
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data xi(t) by using linear interpolation scheme in the preprocessing module in Fig. 3. Next, according to equation (1), the solar radiation correlation coefficient rij between the target location i and the other location j is evaluated every 5 minutes, as given by t
ij
where yˆij (t ) denotes the predicted solar radiation at the target location i using the location j at time t (kW/m2) and Yˆ (t )
outputs of the NNs corresponding to the location I, where a 3-hour duration data yˆ t ' are transformed into a 5-minute basis
x (t ) x x (t ) x
j
j
The gap times where similar solar radiation fluctuations appear are derived by using the SRCA, and the location candidates are selected based on their values. First, the solar radiation data, xi(t) (kW/m2) are generated from yˆ t ' , i.e., the
rij ( )
r ( ) yˆ
(1)
t
where t denotes time (measured in minutes), τ denotes the gap time (min), x denotes the average of x within a period of time (kW/m2). Once the correlation coefficients are calculated using (1), the correlation gap time τ is obtained. The gap times for the other locations are adopted for the solar radiation prediction of the target location i. In other words, the solar radiation data of τ minutes earlier is then used for prediction of the target location. Moreover, to obtain a stable and an accurate prediction result, the above procedure is conducted and performed for all the candidates in real-time, and the analysis data are updated every 5 minutes. The correlation coefficient rij is regarded as the weight factor and the weighted average of the solar radiation prediction can therefore be calculated by using equation (2) as follows:
The same methodology for constructing the NNs as described in Section 3 for the day-ahead PV forecasts is now applied to the real-time PV forecast for individual areas. The difference is in only the data that are utilized. That is, all the data provided every 3 hours by the JMA are transformed into 5minute interval data in the pre-processing section of Fig. 3. The data for the individual locations are then used to construct the NNs, which are used for real-time prediction of the target areas and the other locations. The same procedure as given in steps (ii) to (vi) of Fig. 3 are now performed by using the 5-minute interval data to compute yˆij (t ) in (4). Consequently, the estimation of the real-time PV outputs is obtained by using equation (2). 5. NUMERICAL CASE STUDIES In this section, based on the procedure that was provided in Fig. 3, the performance of our proposed methodology is demonstrated and illustrated through the following two case studies as described below: 1) Local prediction for the PV system at the Hiroshima University (40 kW) using the historical data of the PV output combined with the weather data that are provided by JMA. 2) Global PV generation prediction by using the solar radiation meter data that are provided by the Ministry of Economy, Trade and Industry. For the first case, the day-ahead PV power generation prediction as well as real-time prediction is obtained to show their relative performance and effectiveness. An autoregressive (AR) model is used as a conventional method for performing comparisons. For the second case we compare the accuracy and performance of the proposed prediction scheme with the existing approaches in the literature. 5.1 Day-ahead PV Generation Forecast Results Figures 4 and 5 show the forecast results of a typical day when the PV generation fluctuates significantly. The vertical axis denotes a 30-minute integrated value of the PV power generation. The blue line vertically extending from the predicted value shows the range of ±σ and the red line denotes the range of ± 2σ. The weather marks, which appear at the bottom of the figure, show the corresponding time weather of 3, 6, 9, 12, 15, 18, and 21 from the left (sunny, cloudy, or rainy). When the weather is changing slowly, both results are confirmed to be relatively accurately predicted. For the CI, the measured values are basically within the desired CIs.
Fig. 3. Flowchart of our proposed PV prediction method. 164
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Table I. Prediction errors in June 2012. Proposed method AR method
Av. Error (%) Max Error (%) Av. Error (%) Max Error (%)
The prediction error rate 𝜀𝜀 and the maximum prediction error rate η, as expressed in equations (4) and (5) below, are used as indices for comparing and evaluating the prediction accuracy. The amount of global solar radiation is the theoretical value that represents the ideal maximum amount of solar radiation of the day. Table I shows the average and maximum prediction errors for each target time in June 2012.
Fig. 4. PV forecasting with weather clustering.
Figures 7 and 8 provide the predicted and the actual values of the 60-minutes and the 10-minutes ahead forecasts, respectively. The maximum expected error rate η in the autoregressive (AR) model has greatly been increased over the 5 minutes to the 10 minutes periods. Consequently, the prediction accuracy of the proposed method is almost the same as that of the AR model.
Fig. 5. PV forecasting without weather clustering.
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When the power generation output is small in the morning or in the evening, the CI variance is small whereas the CI has a large variance when the fluctuations become large during the daytime. It can be confirmed that the CI variance is smaller when the weather classification method is applied.
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Moreover, the prediction accuracy is verified for all seasons under different weather conditions by using the data from October-December in 2011 and January-September in 2012 due to their availability. Figure 9 shows the monthly forecast error rate of the 30-minutes ahead forecast. In our proposed method, it is possible to predict in advance fluctuations of the solar radiation to some extent, such as the lamp fluctuations. It can be concluded that our proposed method is capable of suppressing the average error (Av. Error), the maximum error (Max. Error), and MAE for each season compared to the AR model.
Figure 6 shows the transition of the average prediction error and the maximum prediction error for each month. The prediction error is calculated by utilizing the equation (3) as given below (root mean square error: RMSE) where N denotes the data number, y denotes the measured value, and yˆ denotes the predicted value, as follows:
where Xˆ denotes the solar radiation predicted value, Xˆ max denotes the solar radiation predicted value that causes the maximum prediction error, X denotes the solar radiation actual value, Xmax denotes the solar radiation actual value that causes the maximum prediction error, Q denotes the extraterrestrial solar radiation, and Qmax denotes the maximum value of the extra-terrestrial solar radiation.
Fig. 6. RMSE and maximum errors in 2012.
1 N Ps (t ) Pˆs (t ) N t 1
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5 min 10 min 20 min 40 min 60 min ahead ahead ahead ahead ahead 16.09 19.00 20.75 23.76 25.07 16.50 18.47 19.84 21.20 22.26 32.87 36.74 36.56 39.10 40.65 31.43 32.48 33.91 34.89 35.12
(3)
5.3 Comparison of Prediction Performance Performance of our proposed prediction method is now compared with the existing methods in the literature (Yamamoto et al., 1999, Simose et al., 2014) using the solar radiation meter data of the Chugoku region that are provided by the Ministry of Economy, Trade and Industry recorded in the subsidized project “distributed new energy mass introduction promotion system stabilization measures
When the weather classification is performed the average error in most months is shown to be reduced. However, for the maximum error, the increased errors were observed in certain months. In the classification problem, the weather is assumed to be the same for 3 hours and the method could not follow rapid weather changes within 3 hours. 5.2 Real-time PV Generation Forecast Results 165
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6. CONCLUSIONS This paper has proposed a day-ahead as well as a real-time PV generation forecasting methodology for the supplydemand manager module. The performance, capabilities, and effectiveness of our proposed methodology have been verified through extensive simulation case studies. Furthermore, prediction error degree of influence and the CI on the system performance have been validated. There are still a number of research issues that have to be developed for the day-ahead forecast to improve the prediction accuracy, including using a validation data set to examine and overcome the potential over-fitting problem in NN training. Moreover, implementation of our scheme on a simulator to study how significant the impact on prediction errors and/or the CI settings will influence the results should be investigated. For achieving a real-time forecast, it is necessary to take into consideration the relationship between the additional weather conditions beside the solar radiation characteristics. Additionally, effective settings for the CI based on the prediction error analysis and construction of a detection system for performing short-time lamp fluctuations in advance also remain as topics for our future research work.
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REFERENCES
Fig. 8. 10-min ahead predictions. ε (Proposed) (CA)
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Asrari, A., T.X. Wu, and B. Ramos, “A Hybrid Algorithm for Short-Term Solar Power Prediction – Sunshine State Case Study,” IEEE Trans. Sustainable Energy, Vol. 8, No. 2, pp.582-591, Apr. 2017. Bessa, R.J., A. Trindade, and V. Miranda, “Spatial-Temporal Solar Power Forecasting for Smart Grids,” IEEE Trans. Industrial Informatics, Vol. 11, Vo. 1, pp.232-241, Feb. 2015 Hafiz, H.M., N. Yorino, Y. Sasaki, and Y. Zoka, “Feasible Operation Region for Dynamic Economic Dispatch and Reserve Monitoring,” European Trans. on Electrical Power, Vol. 22, No. 7, pp.924-936, 2012. Japan Meteorological Agency: http://www.jma.go.jp/jma/indexe.html Japan Industrial Standard (JIS) C8907, “Estimation method of generating electric energy by PV power system,” 2005. Matsuda, K., K. Arimatsu, K. Yamase, M. Watanabe, J. Yamazaki, and J. Murakoshi, “Short-term Prediction of Photovoltaic Power Generation for Distribution Management System based on Spatial Correlation Analysis,” IEEJ Trans. on Power and Energy, Vol.134, No.9, pp.759-766, Sep., 2014. Ministry of Economy, Trade and Industry (METI), http://www.meti.go.jp/english/ Sasaki, Y., N. Yorino, and Y. Zoka, “Probabilistic Economic Load Dispatch Applied to a micro-EMS Controller,” 19th Power Systems Computation Conference, No. ID574, Jul. 2016. Sasaki, Y., N. Yorino, Y. Zoka, and I.F. Wahyudi, “Robust Stochastic Dynamic Load Dispatch against Uncertainties,” IEEE Trans. on Smart Grid, Vol. PP, No. 99, pp.1-9, 2017 (to appear). Simose, K., H. Ohtake, J.G da Silva Fonseca Jr., T. Takashima, T. Oozeki, Y. Yamada, “Analysis of Error Causes of the Irradiation Forecast by the Japan Meteorological Agency Meso-Scale Model,” IEEJ Trans. on Power and Energy, Vol.134, No.6, pp.518-526, Jun., 2014. Yamada H. and O. Ikki, “National Survey Report of PV Power Applications in JAPAN,” International Energy Agency Photovoltaic Power Systems Programme, issue date: 22 Aug. 2016. Yamamoto, S., T. Katagi, J. Park, and T. Hashimoto, and T. Hashimoto “A Basic Study to Forecast the Power Fluctuation of the Photovoltaic Power Generation by Image Processing of Clouds,” IEEJ Trans. on Power and Energy, Vol.119-B, No.8/9, pp.909-915, Aug./Sep., 1999. Yang, H.T., C.M. Huang, Y.C. Huand, and Y.S. Pai, “A Weather-based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output,” IEEE Trans. Sustainable Energy, Vol. 5, No. 3, pp.917-926, Jul. 2014. Yorino, N., H.M. Hafiz, Y. Sasaki, and Y. Zoka, “High-speed Real-time Dynamic Economic Load Dispatch,” IEEE Trans. on Power Syst., Vol. 27, No. 2, pp.621-630, May 2012. Zhang, Y., M. Beaudin, R. Taheri, and H. Zareipour, “Day-Ahead Power Output Forecasting for Small-Scale Solar Photovoltaic Electricity Generators,” IEEE Trans. Smart Grid, Vol. 6, No. 5, pp.2253-2262, Sep. 2015.
0
Fig. 9. Prediction errors. business” (Ministry of Economy, Trade and Industry, website), and the past weather data by the JMA. A recent study (Yamamoto et al., 1999) has proposed to use cloud images that are observed from ground to improve the prediction accuracy where the MAE of 100-120 (Wh/(m2•h)) is obtained (maximum error is not reported). On the other hand, our method realizes RMSE of 55-80 (Wh/(m2•h)) and MAE of 70-110 (Wh/(m2•h)) as shown in Fig. 6. Another research (Simose et al., 2014) utilizes the cloud images that are obtained from weather satellites to obtain improved predictions where it is reported that the maximum error of 24.5 (%) is obtained (but MAE was not provided). However, our proposed method provides an equivalent performance of the maximum error of 200 (Wh/(m2•h)) and the maximum error rate of 24.8 (%), as provided in Fig. 6. The above conclusions and observations allow one to conclude that in spite of using no complex meteorological imagery and satellites data, our proposed method provides accurate prediction by using only very general data that are commonly available even in disadvantaged areas. 166