Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management

Accepted Manuscript

Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management Tanveer Ahmad , Huanxin Chen , Jan Shair , Chengliang Xu PII: DOI: Reference:

S0140-7007(18)30405-5 https://doi.org/10.1016/j.ijrefrig.2018.10.017 JIJR 4142

To appear in:

International Journal of Refrigeration

Received date: Revised date: Accepted date:

25 May 2018 4 September 2018 4 October 2018

Please cite this article as: Tanveer Ahmad , Huanxin Chen , Jan Shair , Chengliang Xu , Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management, International Journal of Refrigeration (2018), doi: https://doi.org/10.1016/j.ijrefrig.2018.10.017

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Highlights 

Three data-mining models are applied for cooling load demand forecasting



Short and Medium-term intervals are applied for future energy demand forecasting



Environment variables effect on cooling load demand is also analyzed



Model’s performance and accuracy has been estimated at selection of different hidden neurons The best cooling load forecasting performance achieved at the short-term interval

AC

CE

PT

ED

M

AN US

CR IP T



1

ACCEPTED MANUSCRIPT

Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management Tanveer Ahmada, Huanxin Chena*, Jan Shairb, Chengliang Xua, a

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

b

State Key Laboratory of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing,

CR IP T

China

*Corresponding author: Huanxin Chen, E-mail: [email protected]

⁕The short-version of this manuscript was presented at ‘The International Conference on

AN US

Cryogenics and Refrigeration (ICCR-2018)', held on April 12–14, Shanghai, China. This

AC

CE

PT

ED

M

study is containing an abundant addition of the short-version of the conference paper.

2

ACCEPTED MANUSCRIPT Abstract In this study, data-mining techniques comprising three forecasting algorithms for accurate and precise cooling load requirement prediction in the building environment, with the primary aim and the objective of improving the load management are applied. Three stateof-the-art cooling load prediction algorithms are – multiple-linear regression (MLR) model, Gaussian process regression (GPR) model and Levenberg-Marquardt backpropagation neural network (LMB-NN) model. The Pearson correlation analysis is practiced calculating the

CR IP T

correlation between actual cooling load demand and input feature variables of climate parameters. The impact of climate variability on the building load requirement is also analyzed. Forecasting intervals are divided into two basic parts: i) 7-day ahead prediction; and ii) 1-month ahead prediction. To assess the prediction performance, four performance evaluation indices are applied, which are: i) coefficient of correlation (R); ii) mean absolute

AN US

error (MAE); iii) mean absolute percentage error (MAPE); and iv) coefficient of variation (CV). The model’s performance is compared with the selection of different hidden neurons at different load conditions. The MAPE for 7-day ahead prediction interval by MLR, GPR and LMB-NN model is 13.053%, 0.405% and 2.592% respectively. Furthermore, the data-mining

M

algorithms are compared and validated with the previous study, and the MAPE of Bayesian regularization neural networks is calculated 2.515% for 7-day ahead prediction. It was

ED

witnessed that the algorithms could be applied to facilitate the building cooling load prediction, by applying a relatively limited number of parameters related to energy usage as well as environmental impact in the building environment. The forecasting results show that

PT

the three algorithms are effective in predicting the irregular behavior in the data as well as cooling load demand prediction.

load

CE

Keywords: Water source heat pump, data mining models, cooling load prediction, building

AC

1 Introduction

Energy consumption in building environment is an extensively analysed area owing to its

important influence on world energy requirement. Energy intensity (EI) of an office building, which has substantial heating and cooling operation estimates for a ratio of 68.01% [1]. In year 2015, the United States energy requirement in commercial and residential buildings are rendered almost 40.010% of total usage of energy requirement [2]. The usage of energy in a building environment currently estimates for 12.010%, almost one third of the present greenhouse gases (GHGs) ejections in global [3], and 12.010% in the United States of America (USA) [4]. 3

ACCEPTED MANUSCRIPT Kevin et al. [5] conducted a research on projected inclinations of building air conditioning energy requirement and other kinds of loads in various environments. An expanding tendency of cooling load requirement and reducing the tendency of heating load because of the weather change in future perceptive is witnessed. In their effort on comfort standards and weather change, Rajkovich and Kwok [6] summarized that the energy requirement in building precinct estimated about 38.900% of the aggregated load requirement in the USA, that 34.80% was utilized for cooling and heating (C&H) systems. Precise

CR IP T

occupancy forecasting can increase energy efficiency and facility and appliance control of the building sector. Occupancy forecasting by Markove feed-back recurrent model with wireless fidelity in (WIFI) probe advancement and occupancy-based load demand control for an office building to control the cooling load system is conducted in reference [7-8].

Between numerous modern technologies, optimal operation and progressive control has

AN US

been considered a fundamental permissive advancement in decreasing the power usage of air conditioning operations [9]. Although various authors have proposed models to control and model the development and operation of different elements in heating, ventilation, and air conditioning (HVAC) operations [10-11] and acknowledged that precise forecasting of

M

building cooling/heating load is the significant influence in the convincing successful strategies of these applications [12]. For this study, three supervised based data-mining (DM)

ED

models are used to assess the cooling load requirement in an office building environment. The algorithms used in this paper have also been implemented in other areas such as the solar emissions and wind speed prediction. This paper utilizes application of these DM models for

PT

cooling load demand forecasting to maintain the cooling comfort in the building environment. 1.1 Data-mining algorithms

CE

The load forecasting algorithms can be developed from various supervised, unsupervised and ensembles-based approaches. The traditional models are used to forecast and

AC

calculate cooling loads, like as the transfer model [13], Fourier and admittance model (AM) [14], semi-analytical model (SAM) [15] and correlation model (CM) [16-17]. The advancement in energy forecasting models such as modern computer technologies, the Monte-Carlo model (MCM) [18], artificial intelligence-based models (AIBM) [19-20], regression models (RM) [21] and modeling analysis (MA) [22-23] are substantially used in thermal energy forecasting. The numerous popular models that has been developed for load forecasting are artificial intelligence (AI), simulation analysis and regression models (SARM) [24]. Data-mining based algorithms have unique knowledge in obtaining insights from the big

4

ACCEPTED MANUSCRIPT data analysis. Overall, the data-mining models can be categorized into two basic parts, e.g., unsupervised and supervised learning. Supervised based models are the machine learning task of learning a function that outlines an output and an input consists on example output-input sets. The discovery of knowledge is mostly represents applying numerous quantitate and qualitive algorithms. Supervised based models have extensively used for load requirement forecasting [24-27], fault diagnosis and detection [28-29] in the building sector. This research applies the GPR,

CR IP T

MLR and LMB-NN models for predicting the cooling load demand. The GPR algorithm for energy and wind speed prediction were used in reference [30-32]. The prediction results explicate that the developed hybrid approaches (HA) favourably increase wind speed prediction in contrast with different algorithms and the renders adequate period for wind speed prediction. The MLR model (MLRM) for short-term (ST) energy prediction, that is by

AN US

the practice of supporting a United States electric company improve the 1st in house ST energy prediction. The developed algorithm has been worked as a scale (benchmark) for an electric company since 2009. Additionally, it was used for the generation for one-year with comforting efficiency before a significant incline in load [33].

M

Sang et al. [34] practiced multiple artificial approaches (MAA) and the linear regression models (LRM) for the hourly forecasting of the large-scale ground heat pump (LSGHP)

ED

energy requirement and heating performance. These forecasting algorithms can be applied for a minimum period, practiced for load comparisons, verification and measurement of conceivable future real-time load and conservation performance monitoring to indicate the

PT

fault of the system operations. According to Zhao et al., artificial neural networks (ANNs) are extensively employed because of its accuracy and efficient performance in energy forecasting

CE

for building applications [35]. Furthermore, forecasting of domestic building energy usage matches sufficiently including neural networks algorithms than the traditional statistical

AC

algorithms (TSA) such as regression modeling because of the strength to achieve non-linear correlation to create configuration in parallel form which provide continuous computation, to training and learn as well as to execute with relative flexibility and ease [36-37]. In the study, The LMB-NN model is applied to the different hidden neurons selection as well as limited climate data information with objective to improve the forecasting accuracy. Ahmad et al. [42-43] used the supervised based machine learning models for building load demand and district level energy forecasting in short, medium and long-term environment. The results show that the supervised models are easy to apply, develop, interpret in comparison to other various complicated forecasting techniques which have been normally 5

ACCEPTED MANUSCRIPT used for building and district level load forecasting. The supervised fitting of the algorithm includes effects selected from various climate and energy usage data to the model network when the supervised algorithm provides an innovative dependent mechanism [43]. Supervised based models need enough past data and the objective of the algorithm is to extract the historical patterns to forecast the future load demand or requirement. Various researches [45-49] tried to enhance efficiency of financial forecasting from applying outdoors (external) parameters as the model’s input using supervised learning along the past

CR IP T

target market data. A comprehensive review of supervised, unsupervised, white-box approaches, grey-box techniques, physics-based approaches and hybrid approaches was conducted in [50] for building load demand forecasting. From this study, the variety of problem is discovered which includes: end use of various types of building load, rural and urban scales and the matrix of energy performance and forecasting in future perceptive.

AN US

1.2 The contribution of this study

The basic intention of this study is to use the models which support in the area of cooling load demand prediction for medium-term and short-term perceptive. The input features are analysed for various target parameter’s investigations, to achieve higher forecasting results.

M

The objective of this study is to render a methodological way to analyse recorded previous energy usage and executes the medium and long-term forecasting with such data-mining

ED

algorithms. Additionally, a comparison is conducted between the conventional models and data-mining models to observe the energy usage prediction accuracy can be increased with external weather and different energy features parameters information or it relies entirely on

PT

past consumption. This study has applied the actual forecasting, climate and energy consumption data of an office building. The obtained data is sufficient to carry out short and

CE

medium-term forecasting analysis for cooling load demand forecasting in the building environment. The Grubs test for outlier detection and Pearson correlation analysis is applied

AC

to estimate the correlation between net energy usage and climate feature variables. The model’s performance is estimated and examined at different hidden neurons with the performance evaluation index MAE. The rest sections of this paper have the subsequent parts. Section 2 defines the schematic architecture of this study. Section 3 and 4 demonstrate the forecasting approach of model training and testing data sets and forecasting results for ST and MT cooling load demand forecasting. Section 5 demonstrates the conclusion part this study. 2 System structure and proposed methodology

6

ACCEPTED MANUSCRIPT Fig. 1 shows the proposed methodology layout of this study. The research methodology contains the subsequent section such as climate and energy consumption data collection, data normalization and splitting into training and testing data sets, outlier detection, model performance analysis and performance evaluation indices etc. Table 1 demonstrates data statistics of WSHP energy usage (kWh) for one-month and 7-day ahead duration. The subsequent steps are the data preparation for training and testing, the database to store the data-base and moreover classified into three classes, (1) climate prediction parameters; (2)

CR IP T

day of the week, hours and day; and, (3) energy usage data of WSHP. The initially raw data are further investigated to achieve the adequate and clean data for modeling analysis. The Pearson correlation analysis is applied to measure the correlation among numerous climate variables and usage of energy of WSHP. The forecasting error is investigated after training and testing of the models and it must become in the permissible limit otherwise features

AN US

selected as the model’s input are further reorganized for training.

2.1 Variables influencing the energy requirement

Four basic contributing parameters that affect the performance as well as the load

M

demand of building sectors, are -1. prosperities of the building model; 2. lightning, HVAC operation and power distribution system; 3. weather parameters; 4. human behaviour and

ED

planned operations etc. Because of unavailability of data due to factors such as lightning, occupancy space, human behaviour etc., this study focuses on contributing perspectives that assent an explicit influence on the accuracy of energy demand. The main contributing

PT

parameters, that directly influence the cooling load demand are TWBT, TDBT, SR, WD and WS. The TDBT is infiltrated into the building envelope and increase the cooling load demand. Fig. 2

CE

demonstrates the effect of SR and TWBT on energy usage and load requirement of WSHP. Its witnessed that rising the TDBT directly influence and enhance energy usage requirement

AC

because to the substantial expansion in cooling load demand.

Fig.3. demonstrates the coefficient of correlation of different feature parameters including the WSHP energy consumption. The PWL, WS and SR found the best correlation with the net energy consumption of WSHP.

2.2 Supervised based data-mining models

7

ACCEPTED MANUSCRIPT Data-mining models designate the data-mining tasks and assignments which normally combine knowledge domain, especially symmetrical analysis, toward the system process. Several efforts and experimental study have perceived the benefits of combining the knowledge domain in the area of DM based techniques. 2.2.1 The GPR model The GPR model can be practiced identifying different kinds of parameterized as a specific function problem but allows enough more adaptability. Total physical response (TPR) ( ) . In GPR, the amount of function (

(

CR IP T

practices GPR model as an earlier to explain the configuration of specific validation function ) corelates to the input

) is used as random parameters, where

(

). The GPR model is described

as a combination of stochastic parameters at any restricted data which is supposed to be collectively Gaussian disseminated. The GPR model can adequately explain the distribution

AN US

of the data over an anonymous function ( ) from its average function ( ) well as a kernel function (

) which similar to the covariance [( ( )

[ ( )] as

( ))( ( )

( )]. The covariance of Kernel function describes a curvilinear length distance considering that the further approximately designated inputs that would be higher associated in terms of ( () (

M

their value function. Which is, the earlier in the value function is described as: ))

(1)

amount, and (

ED

Where, ( ) is an average function taking the comprehensive course in the validation task ) is a kernel task, practiced defining the covariance approximates. The

function needs the estimation of gamma task to estimate the covariance function among

)

(

(

)

( ) (

))

(2)

CE

(

PT

different points, its supposed to accommodate the problem in the future perceptive.

The automated relevance measurement of kernel task is specified from the variables,

is assigned to as the different signal varies that asses the inclusive covariance

AC

The term

and .

magnitude values. The variable vector

is assigned to as the specific scale

length, which measures the correspondence of the different input variables in (

) , where

represents the amount of input parameters for forecasting the

response . A substantial length of characteristic

intimates the weak significance for the similar input

and vice versa.

2.2.2 Multiple linear regression algorithm

8

ACCEPTED MANUSCRIPT The MLR approach is an addition of simple linear regression models (LRMs), applied to evaluate the relationship between various independent parameters as well as the single consecutive dependent parameter. The MLR model equations can be described as follows: ̂

(3)

Where ̂ is the expected or predicted number of dependent parameters, distinct predictor or independent parameters, through

are

is the amount of Z when each independent

) are almost near to 0, and

through

are the measured

CR IP T

parameter (

through

coefficients of the regression line. Every regression parameter describes the difference in Z corresponding to a single-unit change in each independent parameter. In the MLR circumstances,

, to illustrate the difference in Z corresponding to a single-unit change in

,

containing whole other independent parameters constant (e.g., when independent parameters

AN US

are taken at the equal number or are locked (fixed)). The MLR algorithms provide to investigate the comparative effectiveness of the forecaster, parameters on the subordinates, variables or criterion and these usually multiple sets of data can begin to inaccurate outcomes if they are not investigated accurately. The further benefits are the capability to distinguish anomalies or outliers of the data sets.

M

2.2.3 The Levenberg-Marquardt backpropagation neural network model The basic reason for selecting LMB-NN model is to speed up the process of state ( ) with reverence to variable

minimization function ( )]

)

()

(5) ( ) demonstrates the incline of

( ).

( ) as a sum of squares task

CE

For illustrating ( )

( )

(4)

( ) demonstrates the Hessian model and

where

can be determined as

( )

PT

(

[

ED

converging [38]. The LMB-NN algorithm is explained within Newton’s model, where

∑

( )

(6)

AC

Then the Hessian and gradient matrices can be described as ( )

( )

( ) ( ) ( )

( )

(7) ( )

(8)

Where ( ) explicates the matrix of Jacobian which includes first different derivatives as well as model error in term of the biases and weights, The Jacobian function can be determined as

9

demonstrates the error of the network.

ACCEPTED MANUSCRIPT

( ) [

( )

( )

( )

( )

( )

( )

( )

( )

(9)

( )

]

and ( )

∑

( )

( )

(10)

as [

( ) ( )]

( ) ( )

CR IP T

including the Gauss model, equation (9) becomes 0, the equation number (4) can be described

(11)

Subsequently, the LMB-NN adjustment to the Gauss-Newton model is presented as (

[ )

however,

( ) ( ) ()

[

]

( ) ( )

( ) ( )

( ) ( )

]

is aggregated from variable

( ). Meanwhile, a step decreases

(12) (13)

when a step function would appear in an expanded

( )

AN US

( )

divided from . If

is lower, it displays Gauss-

Newton function. Fig.4 demonstrates the basic structure of LMB-NN model which comprised

2.3 Forecasting evaluation indices

M

different hidden neurons and input sets.

ED

Three forecasting evaluation indices are used to calculate the forecasting accuracy of the data-mining models. The correlation coefficient is the amount that indicates a set of dependence and relationship, among more than two substances in important parameters

PT

[25,39]. The R is a numerical estimation of any kind of relationship, indicating a statistical correlation within two parameters. The higher correlation is considered near to 1 or 1

CE

indicates the best forecasting accuracy. In statistics, to estimate the difference between continuous variables, the MAE is applied. The MAPE also distinguished as a mean absolute

AC

percentage deviation (MAPD), is an estimation of forecasting accuracy of a prediction algorithm in statistics, for illustrating in inclination calculation. With the implementation of R, MAE and MAPE as the performance calculation statistics, the variation between actual and forecasting load demand, correlation and the mean vertical distance between each node can be determined. The standard relative deviation or CV is standardized of variation between two frequency distribution. The CV is used to measure the variation between actual and forecasted energy demand.

10

ACCEPTED MANUSCRIPT ∑ √∑ ∑

√∑

(

̅ )( ̅) ∑

( |

̂ |

∑

|

(

̂ )

̅) (

(14)

̅)

(15) ̂ |

(16)

(17)

̅

is the forecasted energy amount (kWh),

̅ is the

CR IP T

where M is the total data samples,

validated amount and average of energy target data samples (kWh). Where

and ̂ is the

measured and predicted energy demand and shows the total data samples. 3 Model training and validation

The single WSHP total net capacity is 30 kW. It is equipped in an office premises,

AN US

designed and considered follow up the ASHRAE standards [40]. Energy consumption and climate data are divided into testing and training sets with the ratio of 30% and 70% respectively. A total data-samples (2016) for 7-day ahead forecasting and (8928) data points for 1-month ahead forecasting were acquired. The weather parameters are shown in Table 2,

M

like as P24-Avgerage load, PWL, PDL etc., are the model’s input. The energy usage of WSHP for a specific interval is the model’s output. It is obvious that applying the more inputs as the

ED

model’s input achieves the higher forecasting accuracy with the presence of prediction, however, the basic emphasis of this study and identification is also to describe that including

PT

fewer (limited) input parameters, satiating forecasting efficiency can be achieved.

4 Prediction results and model validation

CE

4.1 Forecasting accuracy of data-mining algorithms The primary intention is distributing the forecasting period in two parts is to acquire the

AC

model’s accurate results as well as to achieve more accurate energy prediction requirement of WSHP. In Fig.5, the regression graph shows the upper and lower (±20) bound error of datamining algorithms. Examining the statistical variables of the thoroughgoing model’s performance for the WSHP, the coefficient of correlation R2 amount is higher (0.900) for whole the forecasting upward, aside from 1-month ahead and 7-day forecasting horizon, rendering a precise forecasting, except the multiple linear regression. The forecasting timespan of 1-month ahead and 7-day ahead the mean R2 amount is near to 0.910 and 0.97 respectively, and it’s examined as an immeasurable relationship of the actual energy usage and forecasted ones. Fig.6 demonstrates the graphical representation of WSHP load demand 11

ACCEPTED MANUSCRIPT (LD). The 1-month ahead forecasting performance is precise and similar to the net energy usage except for MLR. The similar forecasting pattern is observed in 7-day ahead prediction.

4.2 The prediction error of forecasting models Its determined that the algorithms with the inadequate energy usage and climate data as model’s input parameter, rendered massive results on prediction performance indices. There

CR IP T

is a small error amid prediction models, aiming at cooling load demand for future perceptive has been observed. Table 3 demonstrates the forecasting variations from three algorithms. Its witnessed that the MLR, GPR and LMB-NN model MAPE for 7-day ahead prediction intervals (13.053%, 0.405%, 2.592%) respectively and higher from 1-month ahead performance. For 7-day ahead forecasting the MAE of GPR, MLR and LMB-NN is 0.193,

AN US

6.544 and 1.316 respectively. The GPR shows higher performance in term of MAE than the MLR and LMB-NN model. The LMB-NN forecasting performance is higher as compared with the MLR model, but lower than the GPR model. The similar trend can be observed for medium-term forecasting. The CV of GPR, MLR and LMB-NN model is 59.154%, 68.215%

M

and 51.364% respectively. It is witnessed that for 1-month ahead forecasting, the LMB-NN

ED

forecasting performance is better than GPR.

Fig. 7 presents the probability forecasting error between actual and predicted energy requirement for 7-days and one-month ahead intervals. The vertical axis demonstrates the

PT

probability error frequency and horizontal axis presents the error frequency. For 7-day ahead forecasting, the MLR error is more moderate than the GRP and LMB-NN models. The

CE

maximum error frequency for 7-day ahead can be observed between (30 to -30), but it is twice

AC

in 1-month ahead forecasting.

The gradient and mu of the LMB-NN model is demonstrated in Fig.8 and its selected of

different hidden neurons. Backpropagation is an approach which is practiced in ANNs models to estimate a gradient which is required in the computation of the biases and weights to be applied in the system. It’s generally employed to train deep ANN models, a term pertaining to NN with higher than 1-hidden nodes and layer. The mu is the training gain and it must be between 0.8-1 in the neural network. In Fig.8 (a) the 7-day ahead forecasting at the selection of 20-hidden neurons is better. The best gradient is observed 10.1797 at 51 epochs and Its

12

ACCEPTED MANUSCRIPT different for 1-month ahead forecasting. The best gradient 0.53677 is found at 108 epochs when the selected hidden neurons were 15 for the model training.

4.3 Model performance compared with existing models The proposed algorithms compared with the previous model, intend a short-term electricity usage in the building sector, and predicting algorithms built on the BRNNs model [41]. A straight contrast may not be pertinent because of the building energy usage pattern are

CR IP T

challenged to observe as well as forecast time duration is also different. Fig.9 demonstrates the model’s comparison with BRNN model and it shows the cooling load demand forecasting performance of data-mining models are better.

Fig.10 explicates the error histogram of training and testing state, training performance

AN US

of BRNN algorithm. It can be witnessed that the training error and the testing error of BRNN model is almost close to zero. It also demonstrates that the training and testing error is low and almost in the permissible limit. Fig.10 (c) shows the training and testing state of the BRNN model. It is depicted that the best training performance of the model is estimated

M

59.060 at 331 epochs.

ED

5 Conclusion

Three supervised based data-mining approaches are applied to optimize and forecast the future cooling load requirement in building environment, especially to estimate net energy

PT

usage and future load requirement of WSHP for energy planning, system balancing and load management. This study can be concluded and analysed with the following aspects The climate and energy consumption data for cooling load demand forecasting are

CE



applied and it is further divided for ST and MT prediction intervals. The climate impact on energy requirement in future perceptive is also analysed.

AC

 

The LMB-NN and BRNN model’s performance is compared with the selection of different layers of hidden neuron.



The GPR forecasting performance is more moderate than the other data-mining models. Slight discrepancy is observed in MLR performance in 7-day ahead performance.



Data-mining models contrasted with the previous BRNN model and it shows that GPR, LMB-NN and WSHP-PC almost contain the similar pattern for 7-day ahead prediction intervals and there is slight variation can be observed for 1-month ahead forecasting. 13

ACCEPTED MANUSCRIPT The best MAE for ST and MT forecasting is witnessed (0.193, 6.544, 1.316) and (0.735, 10.111, 4.632) respectively. The GPR forecasting is more precise and accurate in term of MAPE and CV for ST forecasting except for MT prediction intervals. The LMB-NN model is more accurate MT forecasting. The GPR model captures the algorithm uncertainty and its computational speed is higher. Further, it has the capability to interpolate the training data, have a confidence interval for forecasting and smooth for nonlinear models. The basic advantages of the MLR model are to estimate the relative influence of more than one or one

CR IP T

forecaster parameters to the criteria amount. It has also the capability to find the anomalies and outliers in the random data. The LMB-NN model is more appropriate to solve least squares non-linear problems. The problems especially come in the least squares curve fitting. This study also has some boundaries when forecasting intervals are considered for the longterm perceptive. Increasing the number of data samples, the model’s performance decreases

AN US

because of their specific features and characteristics of the load curve. It is observed that to be obvious to obtain more knowledge, accuracy and information, such as similarities and load patterns at a lower temporal data resolution. The major impact of higher temporal data resolution is that the data volume becomes larger and such a high volume average a higher

M

dimensionality in the data mining, clustering problems that enable the models computationally slower. The implementation of aggregated data can be simple to execute and

ED

reduce the risk losses and enhance the forecasting accuracy. Furthermore, if the climatic environment suddenly shifts on the forecasted day, the forecasting errors are expected to be changed because of sudden changes in cooling or heating load demand. The same pattern of

PT

data sets as the model’s input can be used for heating season to estimate the model’s accuracy and performance. Further, this research would be intended to investigate and discover

CE

connections between these algorithms with the new DM based techniques. Acknowledgement

AC

The authors of this paper respectfully acknowledge the support of 'National Natural Science Foundation of China' (Support Fund Number 51328602 and 51576074) References

[1] R. Jing, M. Wang, R. Zhang, N. Li, Y. Zhao, A study on energy performance of 30 commercial office buildings in Hong Kong, Energy and Building 144 (6) (2017) 117-28. [2] U.S. Energy Information Administration (US EIA). How much energy is 5 consumed in residential and commercial buildings in the United States? United States Department 6 Energy, Washington, DC, United State of America (2016). [3] Nations Environment Program (UNEP), Sustainable building and climate initiative (2016). 14

ACCEPTED MANUSCRIPT [4] U.S. Environmental Protection Agency. Sources of greenhouse gas emissions. 12 EPA headquarters, Washington, D.C., United State of America (2014). [5] K.W. Kevin, H.W. Danny, L. Dalong, C. L. Joseph, Future trends of building heating and cooling loads and energy consumption in different climates, Building and Environment 46 (2011) 223-34. [6] A.G. Kwok, N.B. Rajkovich, Addressing climate change in comfort standards, Build. and Environment 45 (2010)18-22.

CR IP T

[7] P. Yuzhen, A. Rysanek, Z. Nagy, A. Schlüter, Occupancy learning-based demand-driven cooling control for office spaces, Building and Environment 122 (2017) 145-60.

[8] W. Wang, J. Chen, T. Hong, N. Zhuc, Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology, Build. and Envir. 138 (2018) 160-70.

AN US

[9] T. LLC, Energy impact of commercial building controls and performance diagnostics: market characterization, energy impact of building faults and energy savings potential, United

States

Department

of

Energy,

Cambridge,

MA

(2005)

02140-2390.

https://s3.amazonaws.com/zanran_storage/www.tiaxllc.com/ContentPages/42428345.pdf.

M

[10] B.C. Ahn, J.W. Mitchell, Optimal control development for chilled water plants using a quadratic representation, Energy and Buildings 33 (2001) 371-78.

ED

[11] M. Zhenjun, W. Shengwei, Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm, Applied Energy 88 (2010) 19811.

PT

[12] E.B. James, N. Chaturvedi, An Inverse Gray-Box Model for Transient Building Load Prediction, Journal of HVAC & R Research 8 (2002) 73-99.

CE

[13] L. Xiwang, W. Jin, Review of building energy modeling for control and operation, Renewable and Sustainable Energy Reviews 37(2014)517-537.

AC

[14] F. Nelson, A review on the basics of building energy estimation, Renewable and Sustainable Energy Reviews 31 (2014) 53-60.

[15] L. Zhengwei, H. Yanmin, X. Peng. Methods for benchmarking building energy consumption against its past or intended performance: an overview, Applied Energy 124 (2014) 325-334. [16] M.L. Chalal, B. Medjdoub, W. Michael, S. Raid. Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: a review, Renewable and Sustainable Energy Reviews 61 (2016) 761-776.

15

ACCEPTED MANUSCRIPT [17] W. Zeyu, S.R. Srubuvasab, A review of artificial intelligence-based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models, Renewable and Sustainable Energy Reviews 75 (2017) 796-808. [18] Z.X. Hai, M. Federic, Parallel support vector machines applied to the prediction of multiple buildings energy consumption, Journal of Algorithms & Computational Technology 4 (2010) 231-249. [19] X. Wu, V. Kumar, R.J. Quinlan, J. Ghosh, Q. Yang, H. Motoda, et al. Top 10 algorithms

CR IP T

in data mining, Knowledge Information Systems 14 (2008) 1-37.

[20] E.R. Edwards, N. Joshua, P.E. Lynne, Predicting future hourly residential electrical consumption: a machine learning case study, Energy and Building 48 (2012) 591-03. [21] J.K. Rishee, S.M, Kevub, C.J. Patricia, T.E. Johan, Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the

AN US

impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy 123 (2014) 168-178.

[22] W. Zeyu, S.S. Ravi, A review of artificial intelligence-based building energy prediction with a focus on ensemble prediction models 2015 Winter Simulation Conference (2015)

M

3438-3448.

[23] D. Pedro, A few useful things to know about machine learning. Communication ACM 55

ED

(2012) 78-87.

[24] L. Nan, Y. Zheng, B.G. Burcin, T. Chao, C. Nanlin, Why is the reliability of building simulation limited as a tool for evaluating energy conservation measures?,Applied

PT

Energy 159 (2015)196-205.

[25] F. Cheng, X. Fu, S. Wang, Development of prediction models for next-day building

CE

energy consumption and peak power demand using data mining techniques, Applied Energy 127(2014)1-10.

AC

[26] Z. Deyin, Z. Ming, Z. Xu, S. Xing, Energy consumption predicting model of VRV (variable refrigerant volume) system in office buildings based on data mining, Energy 102 (2016) 660-668.

[27] Z. Yaohui, Z. Zijun, K. Andrew, Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms, Energy 86 (2015) 393-402. [28] C. Alfonso, L. Fiorella, K. Imran, Fault detection analysis using data mining techniques for a cluster of smart office buildings, Expert Systems with Applications 42 (2015) 43244338.

16

ACCEPTED MANUSCRIPT [29] X. Fu, F. Cheng, Data mining in building automation system for improving building operational performance, Energy and Buildings 75 (2014) 109-118. [30] C. Niya, Q. Zheng, T.N. Ian, M. Xiaofeng, Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction, in IEEE Transactions on Power Systems 2 (2014) 656-65. [31] J. Hu, J. Wang, Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression, Energy, 93 (2015) 1456-66.

CR IP T

[32] C. Zhang, H. Wei, X. Zhao, T. Liu, K. Zhang, A Gaussian process regression-based hybrid approach for short-term wind speed prediction, Energy Conversion and Management, 126 (2016) 1084-1092.

[33] H. Hong, W. Pu, H.W. Lee, A Naïve multiple linear regression benchmark for short term load forecasting, 2011 IEEE Power and Energy Society General Meeting, San Diego, CA,

AN US

(2011) 1-6.

[34] S. K. Parka, H. J. Moonc, K. C. Mind, C. Hwange, S. Kim, Application of a multiple linear regression and an artificial neural network model for the heating performance analysis and hourly prediction of a large-scale ground source heat pump system, Energy

M

& Buildings 165 (2018) 206-15.

[35] H.X. Zhao, M Frederic, A review on the prediction of building energy consumption,

ED

Renewable and Sustainable Energy Reviews 16 (2012) 3586-3592. [36] K. Rajesh, R.K. Aggarwal, J.D. Sharma, Energy analysis of a building using artificial neural network: a review. Energy and Building 65 (2013) 352-358.

PT

[37] K.A. Merih, U.V. Ismet, Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential

CE

sector, Applied Energy 85 (2008) 271-296. [38] M.T. Hagan, M.B. Menhaj, Training Feedforward Networks with the Marquardt

AC

Algorithm, IEEE Transactions on Neural Networks 5 (1994) 989-93. [39] M.V. Aydinalp, V.I. Ugursal, A.S.Fung, Modeling of the appliance, lighting, and space cooling energy consumptions in the residential sector using neural networks, Applied Energy 71 (2002) 87-110. 6

[40] G.A. Pedro, Z.M. Jesus, Prediction of hourly energy consumption in buildings based on a feedback artificial neural network, Energy and Buildings 37 (2005) 585-601. [41] Y.T. Chae, R. Horesh, Y. Hwang, Y.M. Lee, Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings, Energy and Buildings 111 (2016) 184-94. 17

ACCEPTED MANUSCRIPT [42] T. Ahmad, H. Chen, R. Huang, G. Yabin, J. Wang, J. Shair, H.M.A. Akram, H. Mohsan, M. Kazim, Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment, Energy 158 (2018) 17-32. [43] T. Ahmad, H. Chen, Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment, Energy 160 (2018) 1008-1020. [44] A.N. Kiaa, S. Haratizadeha, S.B. Shouraki, A hybrid supervised semi-supervised graph-

CR IP T

based model to predict one-day ahead movement of global stock markets and commodity prices, Expert Systems With Applications 105 (2018) 159-173.

[45] F. Mohammad, N. Arash, Exchange rate prediction with multilayer perceptron neural network using gold price as external factor, Management Science Letters 2 (2012) 561-70. [46] W. Huang, Y. Nakamori, S.Y. Wang. Forecasting stock market movement direction with

AN US

support vector machine. Computers & Operations Research, 32 (2005)(10) 2513-22

[47] P.K. Narayan, S.S. Sharma, K.S. Thuraisamy, K. S. Can governance quality predict stock market returns? New global evidence. Pacific-Basin Finance Journal, 35(2015) 367-80. [48] B. Weng, M.A. Ahmed, F.M. Megahed. Stock market one-day ahead movement

M

prediction using disparate data sources. Expert Systems with Applications, 79 (2017)15363.

ED

[49] S. Thawornwong. The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing, 56 (2004) 205-32. [50] T. Ahmad, H. Chen, Y. Guo, J. Wang, A comprehensive overview on the data driven and

PT

large scale based approaches for forecasting of building energy demand: A review,

AC

CE

Energy & Buildings 165 (2018) 301-20.

18

ACCEPTED MANUSCRIPT

AC

CE

PT

ED

M

AN US

CR IP T

List of Figures

Fig. 1. The schematic overview of the developed research methodology

19

PT

ED

M

AN US

CR IP T

ACCEPTED MANUSCRIPT

Fig.2. Impact of WS (a & b) and TWBT (c & d) on water source heat pump energy usage with

AC

CE

respect to time 7-day and 1-month duration

Fig.3. Correlation analysis of WSHP energy usage and different input parameters

20

AN US

CR IP T

ACCEPTED MANUSCRIPT

AC

CE

PT

ED

M

Fig.4. Schematic overview of LMB-NN model

Fig.5. Forecasting accuracy of algorithms for 7-day ahead and one-month ahead forecasting

21

CR IP T

ACCEPTED MANUSCRIPT

Fig. 6. The graphical presentation of forecasted and absolute one-month ahead energy

AC

CE

PT

ED

M

AN US

demand

Fig.7. Probability plot of forecasting error between actual and prediction energy requirement for 7-day and 1-month ahead forecasting 22

AN US

CR IP T

ACCEPTED MANUSCRIPT

AC

CE

PT

ED

M

(a)

(b) Fig.8. The gradient and mu of LMB-NN model for (a) 7-day ahead forecasting (b) 1-month ahead forecasting with different hidden neurons

23

AN US

CR IP T

ACCEPTED MANUSCRIPT

M

Fig.9. Data-mining algorithms comparison with existing BRNN model for: a) 7-day; b) 1-

AC

CE

PT

ED

month ahead prediction

24

AC

CE

PT

ED

M

AN US

CR IP T

ACCEPTED MANUSCRIPT

Fig.10. Forecasting performance of BRNN model: a) error histogram; b) training state; c) training and validation error

25

ACCEPTED MANUSCRIPT

List of Tables

Table 1. Performance evaluation indexes of water source heat pump energy usage Mean 55.672 53.728

Standard Dev. 11.490 19.663

Minimum 17.421 0.200

Maximum 80.357 83.700

CR IP T

Session 7-Day (kWh) 1-Month (kWh)

Table 2. The sets of data applied for input and output of algorithms construction

-

LMB-NN-model Inp. Out.

-

AN US

-

MLR-model Inp. Out.

-

-

ED

TDBT (oC) WD (degree) Time (H) SR (w/m2) TWBT (oC) DOW Holidays/working days WS (m/s) PWL (kWh) PDL (kWh) P24-Avgerage (kWh) WSHP-PC (kWh)

GPR-model Inp. Out. -

M

Variables

PT

Table 3. Performance evaluation statistics of the data-mining models

AC

CE

Indices GPR MLR 7-day ahead forecasting statistics R 0.997 0.499 MAE 0.193 6.544 CV (%) 2.051 8.341 MAPE (%) 0.405 13.053 1-month ahead forecasting statistics R 0.987 0.407 MAE 0.735 10.111 CV (%) 59.154 68.215 MAPE 50.408 59.581

26

LMB-NN 0.965 1.316 2.034 2.592 0.862 4.632 51.364 39.125

-