Short-Term Energy Prediction for District-Level Load Management Using Machine Learning Based Approaches

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10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China

Short-Term Energy Prediction for District-Level Load Management 15th International Symposium on District HeatingLoad and Cooling Short-TermThe Energy Prediction for District-Level Management Using Machine Learning Based Approaches Using Machine Learning Based Approaches Assessing Tanveer the feasibility of using the heat demand-outdoor Ahmadaa, Huanxin Chenaa*, Yao Huangaa Tanveer Ahmad , Huanxin Chen *, Yao heat Huangdemand forecast temperature function for a long-term district School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China a

School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

a

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

Abstract a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, Limay, France This research cexplicates an analysis of short-term energy requirement forecasting for78520 district-level by applying machine learning Département Systèmes Énergétiques et Environnement IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France This an analysis of short-term energy requirement forecasting for district-level by applying machine learning (ML)research models. explicates Two ML models employed for energy forecasting which are: I) One-step secant backpropagation neural network (ML) models. Two ML models employed for energy forecasting which are: I) One-step secant backpropagation neuralusage network (OSSB-NN); and II) BFGS Quasi-Newton backpropagation (BFGS-QNB) neural network. The 7-day actual energy and (OSSB-NN); and Quasi-Newton backpropagation (BFGS-QNB) neural The and environmental dataII)areBFGS used for energy forecasting. The Pearson method is used for network. optimizing the7-day sets ofactual inputenergy feature usage variables. environmentalthe datainput are used for parameters energy forecasting. Pearson method is used for optimizing the sets of (FS-I); input feature variables. Furthermore, feature sets are The classified into two basic parts: i) feature selection-I and FS-II. FS-I Abstract Furthermore, input feature parameters sets are classified into limits two basic parts: consumption i) feature selection-I andcontains FS-II. FS-I comprises the the seven environmental parameters, including various of energy data sets(FS-I); and FS-II the comprisessixteen the seven environmental parameters, including variables. various limits energy consumption datathesets and FS-II contains different parameters including the environmental The of Grubbs model is used for outlier’s detection of the District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the different sixteen including the models environmental variables. The Grubbs model is used for the outlier’s detection of and the datasets. Differentparameters hidden neurons of the are selected to measure the forecasting performance at different epochs greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat datasets. Different of thethe models are selected to of measure the forecasting at different epochs and mean absolute errorhidden (MAE).neurons To measure forecasting accuracy the models, coefficient performance of variation (CV) and mean absolute sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, mean absolute (MAE). To measure the forecasting accuracy theof models, coefficient of variationat(CV) mean absolute percentage errorerror (MAPE) indexes are applied. The best MAPE andofCV OSSB-NN and BFGS-QNB FS-IIand state are 0.727%, prolonging the investment return period. percentage (MAPE) indexes are applied. The CV ofare OSSB-NN at FS-II state are 0.727%, 0.496% anderror 1.011%, 0.667% respectively, when thebest fourMAPE hiddenand neurons selected.and TheBFGS-QNB ML algorithm’s accuracy is validated The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand 0.496% and 1.011%, when the four hidden neurons(LMB) are selected. algorithm’s accuracy is validated and compared with 0.667% existing respectively, Levenberg-Marquardt backpropagation model.The It ML is demonstrated that including the forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 and compared with asexisting backpropagation (LMB) with model.theIt LMB is demonstrated that including the utilization of FS-II one ofLevenberg-Marquardt the model’s input variables, and comparison model, the energy forecasting buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district utilization ofisFS-II as accurate one of and the can model’s input variables, and comparison with the LMB model, the energy forecasting performance precise, be increased. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were performance is precise, accurate and can be increased. compared with results from a dynamic heat demand model, previously developed and validated by the authors. Copyright © 2018 Elsevier Ltd. All rights reserved. The results showed that when only weatherLtd. change is considered, the margin of error could be acceptable for some applications © 2019 The Published by Elsevier Copyright ©Authors. 2018 Elsevier Ltd. Allresponsibility rights reserved. Selection and peer-review under of the scientific committee of the 10th International Conference on Applied (theiserror in annual was lower 20% for license all weather scenarios considered). However, after introducing renovation This an open accessdemand article under the CCthan BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/) th Selection and peer-review under responsibility of the scientific committee of the 10 International Conference on Applied Energy (ICAE2018). Peer-review under responsibility of theupscientific committee of ICAE2018 – Theand 10threnovation International Conference on Applied Energy. scenarios, the error value increased to 59.5% (depending on the weather scenarios combination considered). Energy (ICAE2018). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the Keywords: Machine learning; District-level; Energy forecasting; Energy efficiency decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Keywords: Machine learning; District-level; Energy forecasting; Energy efficiency renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and 1.improve Introduction the accuracy of heat demand estimations.

1. Introduction theThe recent era,Published the substantial number © In 2017 Authors. by Elsevier Ltd. of studies gave more consideration to analyzing and evaluating the In the recent the substantial number of studies more consideration toasanalyzing and evaluating the energy efficiency throughout the [1]. Energy prediction considered essential it renders forecasts ofand energy Peer-review underera, responsibility of globe the Scientific Committee ofgave Theis15th International Symposium on District Heating energy efficiency throughout the globe [1]. Energy prediction is considered essential as it renders forecasts of energy Cooling. Keywords: Heat demand; Forecast; Climate change

* Corresponding author. E-mail address:author. [email protected], [email protected] * Corresponding E-mail address: [email protected], [email protected] 1876-6102Copyright © 2017 The Authors. Published byrights Elsevier Ltd. 1876-6102 © 2018 Elsevier Ltd. All reserved. Peer-review under responsibility of theLtd. Scientific Committee of The 15thof International Symposium on DistrictonHeating Cooling. Selection under responsibility the scientific committee the 10th International Conference Appliedand Energy (ICAE2018). 1876-6102 Copyright © 2018 Elsevier All of rights reserved. 1876-6102and © peer-review 2019 The Authors. Published by Elsevier Ltd. Selection peer-review under under responsibility the scientificlicense committee of the 10th International Conference on Applied Energy (ICAE2018). This is anand open access article the CCofBY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.967

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requirement for future energy usage [2] and is necessary for all electric companies in order to manage the supply and demand balance and power system stability [3]. The hybrid models are proposed in numerous studies for short-term (ST) energy usage requirement. However, the support vector machine (SVR) algorithms are considered to achieve a globally best energy forecasting solution, preferably poor interpretation capability of the black box in artificial neural networks (ANN) [4]. The general techniques applied for forecast can be divided into artificial intelligence (AI) and statistically based approaches. The AI-based approaches include ANN, SVM, genetic models and fuzzy logic, however, the statistical forecasting models are the techniques that examine to compare the energy required for its causal impacts into mathematical algorithms. An illustration of such kinds of algorithms could be the Kalman filters, multiple regression approaches and autoregressive moving average [5-7]. Energy forecasting models can be categorized into three basic parts: grey box, white box and black box [8]. When sufficient climate and energy consumption data are accessible, data-driven black-box and grey box algorithms consider an exceptional part. The black-box algorithms are further categorized such as the linear autoregressive algorithms (ARA), as shown by [9]. Touretzky et al. [10] proposed an ARA algorithm to predict the energy required for energy management in the building sector, specifically demand response and supervisory control. Moreno et al. [11] examined that to what degree ARA and autoregressive with exogenous inputs (ARAX), could be applied for energy prediction in the building internal air-temperature. From this study, they examined, the ARA models render a reliable forecast of the building indoor temperature than the ARAX algorithms. Furthermore, including non-linear black-box algorithms as nonlinear autoregressive network with exogenous inputs (NARAX) have been applied for this purpose in reference [12]. Francesco et al. [13] assessed various complex algorithms to precisely estimate hourly energy usage requirement for a district energy management. They discovered out that a NARAX gives the excellent fit to data. A comprehensive study of the data-driven and large-scale based approaches for energy forecasting and management, the future load demand, short, medium and long-term energy forecasting is shown in reference [1416]. These studies conduct in-extent consideration in the energy management algorithms in the building environment and recent progress in energy prediction approaches used to predict the future energy usage and requirement. According to the above discussion, the literature review compiles that the various studies proposed that the ML models can measure the properties of the nonlinear behavior of different features sets such as if they are available in the form of energy usage and climate data and the numbers of neurons of ANN-based models are determined best. In this research, the two ANN-based algorithms are used for district-level energy demand forecasting which are: i) OSSB-NN; and ii) BFGS-QNB. The chosen ANN algorithms in this research are practiced by different researchers with various kinds of tasks such as wind speed prediction, solar radiation forecasting, but in the study, we applied for district level energy demand forecasting with different task and the forecasting results are better with chosen the two-input feature selection state FS-1 and FS-2 data information in short-term district-level energy prediction. 2. Proposed methodology

Fig.1. Proposed methodology

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Fig. 1 shows the forecasting methodology used to construct the energy forecasting models. The research work comprises into three basic types: 1) energy usage and environmental data collection, collection; 2) data preprocessing; and 3) testing, validation and training of forecasting algorithms. 2.1. Weather and energy consumption data The environmental effect on power usage can be substantial [17]. The forecasting algorithms in this research, validated and trained using the actual estimated energy usage and climate data for district level energy predictions. The Independent System Operator (ISO-New England) and Surface Synoptic Observations (SYNOP) weather location provide and measure 30-minutes intervals energy usage data and one-hour weather data to assess the environmental conditions of prescribed intervals for investigation and it can be further applied for energy forecasting [18-19]. Seven environmental variables were applied to forecast the future energy requirement. Fig.2 explicates the system load (SL) and weather variables. The SL shows the average SL, peak SL and energy usage at different hours in one week.

Fig.2. Climate variables, system average and peak system load consumption 2.2. Outlier detection The Grubbs test is used for determining the outliers in bound series sequence such as the time-series. The Grubbs test identifying the outliers of data set that are chosen in following sequence z1  z2  z3  ...zo . This explicates that Grubbs method determines the data sample numbers. 2.3. Machine learning models In this study two supervised based machine learning models are applied for short-term ahead energy forecasting. The OSSB-NN is an effort to link the rift among the OSSB-NN and conjugate gradient algorithms (CGA). The OSSB-NN model requires less computation per epoch and storage. It needs insignificantly more computation per epoch and storage than the CGA. (1) Y= Y= b * eY where eY shows the search direction, the parameter b is used to decrease the performance simultaneously with the search direction. Y demonstrates the bias and weight parameters. The BFGS-QNB model is a choice for the CGA for rapid optimization.

Yl == yl − Bl−1hy 1

(2)

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Where Bl is the Hessian matrix of the efficiency ratio at the biases as well as the present total weight numbers. Where hy is the gradient and yl is an estimated Hessian matric of the gradient. This model needs further computation in every iteration and further area than the CGA, however, it usually concentrates in several iterations. The estimated Hessian need to be saved, and its dimension is o*o, where o is similar to the biases and weights and in the system. For short-term energy forecasting, BFGS-QNB can be an effective for training function. Table 1 shows the variables for OSSB-NN and BFGS-QNB model training and validation of the ML models. Table 1. Variables for OSSB-NN and BFGS-QNB model OSSB-NN BFGS-QNB Function Range Function Maximum epochs to train 1000 Maximum epochs to train Maximum validation failure 6 Maximum validation failure −10 Minimum gradient performance Minimum gradient performance 1e Epochs among displays Maximum step size Maximum step length Step size in initial intervals

25 26 100 0.01

Epochs among displays Maximum step size Maximum step length Step size in initial intervals

Range 1000 6

1e−10

26 25 100 0.001

2.4. Performance evaluation indices To examine the efficiency and performance of the forecasting algorithms quantitatively, two performance indices: MAPE and CV are practiced. The MAPE refer to a scale-independent index, offering a straightforward method to describe the accuracy of the algorithms [20]. The CV demonstrates the difference amid forecasted and actual energy requirement from a system operation [21]. 3. Parameters selection for load demand forecasting 3.1. Main factors affecting energy requirement The weather changes which leaves the direct impacts on energy usage is the temperature (TDEW, TDBT and TWBT), humidity, precipitation, wind speed, wind chill index, cloud cover, solar radiation and intensity of light, etc., Different circumstances like as type of buildings, socio-demographic data and different climate variables can further enhance the prediction intervals. Because of the limited of related environmental parameters for target, training and validation, some other variables such as building characteristics, type of building, etc., are out of range (scope) of this research. Table 2 demonstrates the descriptive statistics of various input climate parameters. Table 2. Descriptive statistics of various input parameters Input variables TDBT (°F) TDEW (°F) RHumidity (%) Wd (Degree) Ws (m/s) CLDFraction MSLP

Average 30.628 21.571 73.494 181.071 9.458 0.539 29.899

Min. 17.00 5.000 29.000 0.000 0.000 0.000 29.400

Max. 46.000 38.00 96.000 360.00 24.000 1.000 30.460

Std. Dev. 7.553 10.673 17.083 104.846 5.645 0.457 0.185

Median 31.000 20.000 79.000 210.000 9.000 0.700 29.905

3.2. Training and validation data sets For algorithms’ training and validation, the 30% data is covered/hidden through the validation and the remining 70% data samples are used for the training of the models. The SL and environmental data are further categorized in two basic states: FS-I) with SL and 7 input weather parameters; and FS-II) with SL and 16 input weather variables.



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Table 3 explicates the training of forecasting models. The input features selection of state FS-I are dry bulb temperature (TDBT), Months, dew point temperature (TDEW), Hours, previous day load (PDLoad), relative humidity (RHumidity) and previous week load (PWLoad). The selection of state FS-II is real time demand (RTDEMAND), TDBT, TDEW, RHumidity, day-ahead demand (DADEMAND), wind speed (Ws), wind direction (Wd), mean sea level perception (MSLP), clouds fraction (CLDFraction), real-time congestion component (RTCC), real-time energy consumption (RTEC), PDLoad, real-time marginal loss component (RTMLC), system load (SLoad) and PWLoad. The DADEMAND, RTDEMAND, RTEC, RTMLC and RTCC doesn’t use in the existing studies, and these variables are new for energy requirement forecasting. Table 3. Training data sets Inputs FS-1 FS-2

Variables TDBT, TDEW, Months, Hours, RHumidity, PDLoad, PWLoad TDBT, RTDEMAND, TDEW, DADEMAND, RHumidity, Wd, Ws, CLDFraction, MSLP, RTEC, RTCC, RTMLC, PDLoad, PWLoad , SLoad, Hours

Number 7

Output SL

16

SL

4. Modeling results and discussion 4.1. Forecasting results The performance of district level energy usage prediction comprises on the accuracy of the different of inputs in the form of data. Fig.3, demonstrates graphically actual and predicted load profile for the 7-day ahead forecasting of OSSB-NN and BFGS-QNB model. The different hidden neurons are selected to foresee the accurate of energy prediction. In Fig.3 (a), the OSSB-NN performance is similar at different hidden neurons except FS-N-4. In Fig.3 (b), BFGS-QNB forecasting performance is more accurate and precise at selection of different hidden neurons.

Fig.3. System load and forecasted energy requirement of OSSB-NN and BFGS-QNB model at state (a) FS-I, and (b) FS-II

Fig.4. CV and MAPE performance with different hidden neurons selection

Fig.5. MAE performance with different hidden neurons selection

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The evaluation of ML models comprises of following indexes: the MAPE and CV. Fig.4 demonstrates the MAPE and CV at CV-FS-I, FS-II and MAPE-FS-I, FS-II state. It is observed that the FS-II state forecasting results are better than FS-I at different selected hidden neurons. The CV and MAPE of the OSSB-NN model increases till hidden neuron 7 and gradually decreasing when the number of hidden neurons is increased, but the different pattern is observed in CV-FS-II and MAPE-FS-II state. Fig.5. demonstrates the mean absolute error at selection different hidden neurons. The OSSG-NN-FS-II and BFGS-QNB-FS-II state error is lower as compared with OSSG-NN-FS-I and BFGS-QNB-FS-I. The best MAE is observed of OSSG-NN-FS-II and BFGS-QNB-FS-II at selected hidden neuron 4. Fig.6. shows the training, validation and testing of OSSB-NN in FS-I state and Fig.7.

Fig.6. Training, validation and testing of OSSB-NN in FS-I state

Fig.7. Training, validation and testing of BFGS-QNB in FS-I and FS-II state demonstrates the training, validation and testing of BFGS-QNB in FS-I and FS-II state. In this figure the vertical axis depicts the forecasting error and horizontal axis explicate best performance at different numbers of epochs. The gradient is decreased or an increase in the quantity of a characteristic (e.g., climate variables, system load or concentration) observed in passing from one duration or moment to another.

Fig.8. Gradient of OSSB-NN and BFGS-QNB model



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Fig.8. demonstrates the gradient of OSSB-NN and BFGS-QNB models. FS-II-N-6, FS-II-N-9 of OSSB-NN model and FS-I-N-5, FS-I-N-13 of BFGS-QNB model in Fig.8 explicate the gradient at different epochs. The best gradient 225604.978, 342615.894 at epochs 38, 30 is depicted of OSSB-NN model at N-6 and N-9 selected hidden neurons respectively. 4.2. Model’s performance comparison The applied energy forecasting models in this study, contrasted with previous study, which used the model training using LMB model [22]. There is minor variation might be observed in forecasting comparison because the region, forecasting season and data is different from our study. Table 4 shows the LMB performance comparison with OSSB-NN and BFGS-QNB with respect to MAPE and CV at different hidden neurons (HN). It’s witnessed that the ML algorithm forecasting performance is higher and similar with LMB algorithm.

HN 4 5 6 7 9 11 13

Table 4. ML model’s comparison with existing LMB model OSSB-NN BFGS-QNB LMB FS-I (%) FS-II (%) FS-I (%) FS-II (%) FS-I (%) FS-II (%) CV MAPE CV MAPE CV MAPE CV MAPE CV MAPE CV MAPE 8.912 7.294 1.011 0.727 4.338 3.679 0.667 0.496 4.338 3.679 0.099 0.054 8.648 6.495 1.488 1.184 4.024 2.902 0.853 0.617 4.024 2.902 0.136 0.098 7.574 6.035 2.005 1.673 3.681 2.759 1.331 0.660 3.681 2.759 0.116 0.065 7.183 5.455 2.616 2.05 3.261 2.5862 1.502 1.120 3.261 2.586 0.119 0.081 5.357 4.162 2.230 1.821 2.923 2.276 2.156 1.577 2.923 2.276 0.103 0.062 3.132 2.619 1.565 1.284 2.383 1.926 2.115 1.430 2.383 1.926 0.130 0.082 2.601 1.953 1.437 1.154 3.458 2.650 1.151 1.211 3.458 2.650 0.155 0.136

5. Conclusion This study examines the modeling of future district-level energy prediction using by two ML models which are OSSB-NN and BFGS-QNB algorithms. The usage of two ML based model for district-scale level energy forecasting for 7-day ahead intervals is examined. Further model inputs are divided into two parts FS-I and FS-II with different climate and energy usage feature variables. The Pearson correlation analysis and Grubs test are applied to find the correlation between SL and different input features as well as outlier’s detection respectively. The SL is obtained from ISO New England data management system over the one-month duration is obtained for prediction and validation of the models. The different hidden neurons are selected to choose the better model’s performance. The MAE is estimated with different feature selection at FS-I and FS-II state. Its observed that the BFGS-QNB model performance is better than OSSB-NN from FS-I and FS-II state. The FS-II results are better than FS-I of BFGSQNB as well as OSSB-NN model. The best MAPE and CV is observed in FS-II state of BFGS-QNB model when the hidden four neurons are selected. The gradient of OSSB-NN and BFGS-QNB model at different epochs are analyzed. Mean square error of training, validation and testing state at different iterations are also investigated. The forecasting models are compared with the exiting LMB model and the results in FS-I and FS-II state are almost similar with BFGS-QNB model. There is slight variation can be observed, comparison made with OSSB-NN model. It’s because the different features of inputs data sets, duration and forecasting intervals is also different. By compare with the LMB model in term of CV and MAPE, the ML algorithm’s efficiency and performance is similar like the existing model. In future, this study will be massively expanded to forecast the medium and long-term energy forecasting with different climate data sets in different environment. Acknowledgements The authors appreciatively acknowledge the assistance of National Natural Science Foundation of China (Grant 51576074 and 51328602).

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