Short-Term Load Forecasting and Associated Weather Variables Prediction Using ResNet-LSTM Based Deep Learning

Short-term load forecasting is mainly utilized in control centers to explore the changing patterns of consumer loads and predict the load value at a certain time in the future. It is one of the key technologies for the smart grid implementation. The load parameters are affected by multi-dimensional factors. To sufficiently exploit the time series characteristics in load data and improve the accuracy of load forecasting, a hybrid model based on Residual Neural network (ResNet) and Long Short-Term Memory (LSTM) is proposed in this paper. First, the data with multiple feature parameters is reconstructed and input into ResNeT network for feature extraction. Second, the extracted feature vector is used as the input of LSTM for short-term load forecasting. Lastly, a practical example is used to compare this method with other models, which verifies the feasibility and superiority of input parameter feature extraction, and shows that the proposed combined method has higher prediction accuracy. In addition, this paper also carries out prediction experiments on the variables in the weather influencing factors.