A Novel Data-Driven Method With Decomposition Mechanism Suitable for Different Periods of Electrical Load Forecasting

Abstract

For improving the precision of the load forecasting in different time spans, a new load forecasting model which combines the improved complete ensemble empirical mode decomposition algorithm based on adaptive noise (ICEEMDAN) algorithm, the least squares support vector machine (LS-SVM) and the long short-term memory network (LSTM) is proposed. In this paper, the training set of the forecasting model is acquired from the department of dispatch center from a large city in the north of China. And the advantages of the forecasting algorithms and decomposition algorithm are applied reasonably, where the ICEEMDAN algorithm is used to decompose the original historical load data. By using the ICEEMDAN algorithm, the fluctuation trend of different periods can be obtained. And the structure of the neural network combining LS-SVM and LSTM, which is used to obtain the load forecasting result. Based on LS-SVM and LSTM algorithm the non-stationary and stationary signals have been processed, respectively. The simulation results show that whether testing by the dataset acquired from a city in the north of China or the Elia dataset, the proposed method outperforms the load forecasting model in the short-, medium- and long-term which is based on PSO-SVR, LS-SVM, LSTM and ICCEMDAN-LSTM, respectively.

Keywords

• Load forecasting
• improved complete ensemble empirical mode decomposition algorithm based on adaptive noise
• least squares support vector machine
• long short-term memory network