Application of residual self-fitting ensemble neural network based on LSTM in short-term power load forecasting

Abstract

Accurate load forecasting helps to improve the safety and stability of power systems. LSTM neural networks based on deep learning has lower predicted error than traditional machine learning and time series models. After calculating the residuals of the predicted values and observed values of the LSTM and traditional machine learning model, it can be found that the distribution of residuals also possessed with the regularity of time series data. Therefore, the residual time series can be fitted by another model based on this regularity. To avoid the limitations of a single model, a variety of neural network models have been used to compose ensemble model. In this model, the residual can be generated and fitted by itself. In other words, it implements self-fitting. After analysing the residual distribution and the ensemble method of predictive model, this paper presents a new ensemble neural network model used for short-term power load forecasting. After the experiment, it's obvious that the predicted results of the new ensemble neural networks is more accurate than single LSTM.