Short‐term building load forecast based on a data‐mining feature selection and LSTM‐RNN method,IEEJ Transactions on Electrical and Electronic Engineering

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Short‐term building load forecast based on a data‐mining feature selection and LSTM‐RNN method
IEEJ Transactions on Electrical and Electronic Engineering ( IF 1 ) Pub Date : 2020-05-28 , DOI: 10.1002/tee.23144
Gaiping Sun ₁ , Chuanwen Jiang ₁ , Xu Wang ₁ , Xiu Yang ₂

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Short‐term load forecast for individual electric customers is becoming increasingly important in the grid operation, since the power system is becoming a more interactive and intelligent system. Accurate short‐term load forecast for industrial or commercial electric buildings is more challenging due to the complicated load characteristics and numerous influence variables. In this paper, we consider maximizing the relevancy and minimizing the redundancy criterion to select effectively feature variables, which influence the building load consumption, and then a deep learning technique—long‐short memory recurrent neural network is proposed to predict the load consumption. This novel strategy captures distinct load characteristics, choosing accurate input variables, and shows a great forecasting performance as demonstrated by three different types of city building load in China. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

更新日期：2020-05-28

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