A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior

Abstract With the growth of residential load and the popularity of intelligent devices, resident users have become important target customers for demand response (DR). However, due to the strong volatility of individual household load and the large difference in user’s behavior, the accuracy of residential load forecasting is generally low and the forecasting effect is unstable, which is not conductive to the implementation of DR. To improve the accuracy of residential load forecasting, this paper proposes a combined deep learning load forecasting model considering multi-time scale electricity consumption behavior of single household resident user to achieve high-accuracy and stable load forecasting. Aiming at the electricity consumption behavior, the multi-time scale similarity analysis is carried out. For the time scale of one year, Normalized Dynamic Time Warping (N-DTW) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are used to analyze the significance of single user's long-term electricity consumption behavior. For the time scale of 7 days, behavior similarity is used to analyze the consistency of single user's short-term electricity consumption behavior. Then, Mutual Information (MI) and Principal Component Analysis (PCA) are used to select features and reduce dimensions of multi-dimensional weather influencing factors, so as to avoid the interference of irrelevant factors and improve the calculation speed. On this basis, combined with Back Propagation (BP) neural network, Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) neural network, a combined deep learning network load forecasting model (Co-LSTM) is constructed by using multi-model and multi-variable method to achieve stable and high-accuracy load forecasting. Finally, based on the actual load data from the American Pecan Street Energy Project, the forecasting accuracy of the proposed model of resident user is evaluated. From the performance of load forecasting for 42 target users, the minimum, maximum and average Mean Arctangent Absolute Percentage Error (MAAPE) of Co-LSTM is 18.70%, 45.95% and 31.20% (the average MAAPE is 4.97% less than the traditional LSTM model) respectively.