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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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    Cited by:

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    4. Jiakang Wang & Hui Liu & Guangji Zheng & Ye Li & Shi Yin, 2023. "Short-Term Load Forecasting Based on Outlier Correction, Decomposition, and Ensemble Reinforcement Learning," Energies, MDPI, vol. 16(11), pages 1-16, May.
    5. Lu, Shixiang & Xu, Qifa & Jiang, Cuixia & Liu, Yezheng & Kusiak, Andrew, 2022. "Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network," Energy, Elsevier, vol. 242(C).
    6. Alfredo Candela Esclapez & Miguel López García & Sergio Valero Verdú & Carolina Senabre Blanes, 2022. "Automatic Selection of Temperature Variables for Short-Term Load Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    7. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    8. Filipe Rodrigues & Carlos Cardeira & João M. F. Calado & Rui Melicio, 2023. "Short-Term Load Forecasting of Electricity Demand for the Residential Sector Based on Modelling Techniques: A Systematic Review," Energies, MDPI, vol. 16(10), pages 1-26, May.
    9. Zheyu He & Rongheng Lin & Budan Wu & Xin Zhao & Hua Zou, 2023. "Pre-Attention Mechanism and Convolutional Neural Network Based Multivariate Load Prediction for Demand Response," Energies, MDPI, vol. 16(8), pages 1-13, April.
    10. Wu, Jiahui & Wang, Jidong & Kong, Xiangyu, 2022. "Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning," Energy, Elsevier, vol. 256(C).
    11. Yijun Wang & Peiqian Guo & Nan Ma & Guowei Liu, 2022. "Robust Wavelet Transform Neural-Network-Based Short-Term Load Forecasting for Power Distribution Networks," Sustainability, MDPI, vol. 15(1), pages 1-17, December.
    12. Mingping Liu & Xihao Sun & Qingnian Wang & Suhui Deng, 2022. "Short-Term Load Forecasting Using EMD with Feature Selection and TCN-Based Deep Learning Model," Energies, MDPI, vol. 15(19), pages 1-22, September.
    13. Fazlipour, Zahra & Mashhour, Elaheh & Joorabian, Mahmood, 2022. "A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism," Applied Energy, Elsevier, vol. 327(C).
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    16. Yin, Chen & Mao, Shuhua, 2023. "Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting," Energy, Elsevier, vol. 269(C).

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