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Electrical load-temperature CNN for residential load forecasting

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  • Imani, Maryam

Abstract

Residential load forecasting is a challenging problem due to complex relations among the hourly electrical load values along the time and also nonlinear relationships among the consumed electricity values and their associated temperature values. A nonlinear relationship extraction (NRE) method is proposed in this work. NRE obtains a load cube where each hourly load value is surrounded by load values of past, present and future hours in previous, same and next days of the same week and previous week. Then, a convolutional neural network (CNN) is used to extract the nonlinear relationships among the load values. In addition, a load-temperature cube is composed from the hourly load and temperature values of a week. Another CNN is trained by using the load-temperature cubes to learn the hidden nonlinear load-temperature features. The extracted features are given to a support vector regression (SVR) for load forecasting. The two dimensional convolutional operator is utilized for local feature extraction from the neighborhood regions; the nonlinear activation function is used for nonlinear feature extraction; and the SVR with Gaussian kernel is employed for minimizing the forecasting error. The forecasting results show the superior performance of the proposed method compared to several outstanding forecasters.

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  • Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s0360544221007295
    DOI: 10.1016/j.energy.2021.120480
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    1. Bessec, Marie & Fouquau, Julien, 2008. "The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach," Energy Economics, Elsevier, vol. 30(5), pages 2705-2721, September.
    2. Prado, Francisco & Minutolo, Marcel C. & Kristjanpoller, Werner, 2020. "Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework," Energy, Elsevier, vol. 197(C).
    3. Zhang, Guoqiang & Guo, Jifeng, 2020. "A novel ensemble method for hourly residential electricity consumption forecasting by imaging time series," Energy, Elsevier, vol. 203(C).
    4. repec:dau:papers:123456789/8180 is not listed on IDEAS
    5. Tran, Duc-Hoc & Luong, Duc-Long & Chou, Jui-Sheng, 2020. "Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings," Energy, Elsevier, vol. 191(C).
    6. Shepero, Mahmoud & van der Meer, Dennis & Munkhammar, Joakim & Widén, Joakim, 2018. "Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data," Applied Energy, Elsevier, vol. 218(C), pages 159-172.
    7. Xie, Guangrui & Chen, Xi & Weng, Yang, 2020. "Input modeling and uncertainty quantification for improving volatile residential load forecasting," Energy, Elsevier, vol. 211(C).
    8. Chi, Fang'ai & Xu, Liming & Pan, Jiajie & Wang, Ruonan & Tao, Yekang & Guo, Yuang & Peng, Changhai, 2020. "Prediction of the total day-round thermal load for residential buildings at various scales based on weather forecast data," Applied Energy, Elsevier, vol. 280(C).
    9. Yin, Linfei & Xie, Jiaxing, 2021. "Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems," Applied Energy, Elsevier, vol. 283(C).
    10. Yi-Tui Chen, 2017. "The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    11. Lee, Chien-Chiang & Chiu, Yi-Bin, 2011. "Electricity demand elasticities and temperature: Evidence from panel smooth transition regression with instrumental variable approach," Energy Economics, Elsevier, vol. 33(5), pages 896-902, September.
    12. Li, Yanying & Che, Jinxing & Yang, Youlong, 2018. "Subsampled support vector regression ensemble for short term electric load forecasting," Energy, Elsevier, vol. 164(C), pages 160-170.
    13. Imani, Maryam & Ghassemian, Hassan, 2019. "Residential load forecasting using wavelet and collaborative representation transforms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    15. Haben, Stephen & Giasemidis, Georgios & Ziel, Florian & Arora, Siddharth, 2019. "Short term load forecasting and the effect of temperature at the low voltage level," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1469-1484.
    16. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Duan, Jizheng & Chang, Mingheng & Chen, Bolong, 2021. "Short-term wind speed forecasting using recurrent neural networks with error correction," Energy, Elsevier, vol. 217(C).
    17. Zhang, Wenjie & Quan, Hao & Srinivasan, Dipti, 2018. "Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination," Energy, Elsevier, vol. 160(C), pages 810-819.
    18. Talaat, M. & Farahat, M.A. & Mansour, Noura & Hatata, A.Y., 2020. "Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach," Energy, Elsevier, vol. 196(C).
    19. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    20. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    21. Henley, Andrew & Peirson, John, 1997. "Non-linearities in Electricity Demand and Temperature: Parametric versus Non-parametric Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(1), pages 149-162, February.
    22. Zhang, Guoqiang & Guo, Jifeng, 2020. "A novel ensemble method for residential electricity demand forecasting based on a novel sample simulation strategy," Energy, Elsevier, vol. 207(C).
    Full references (including those not matched with items on IDEAS)

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