Short-term household load forecasting based on Long- and Short-term Time-series network

Abstract

Focusing on the issue of significant randomness and low latitude of short-term household electrical load data, this paper proposes a novel short-term load multi-step forecasting method based on long-and short-term time series network (LSTNet). Firstly, the time sliding window method is used to sample massive historical load data to construct feature maps as input. Secondly, convolutional neural network (CNN) and long short-term memory (LSTM) are used to capture temporal short-term local information and long-term related information respectively, and autoregressive (AR) models are used as linear components. Then, the models will be evaluated using a scheme called walk-forward validation, and the average absolute percentage error (MAPE) and root mean square error (RMSE) are used as accuracy evaluation indicators. Finally, the four-year electric load data of a family in Paris, France is used to verify the proposed method and comprehensively compare the proposed method with the three most popular load forecasting algorithms. The experimental results show that in the prediction results for the next week, the MAPE and RMSE of the prediction method proposed in the paper are smaller than those of other algorithms, which can more effectively express the time series relationship of household short-term load and have higher prediction accuracy.