An ensemble multi-step M-RMLSSVR model based on VMD and two-group strategy for day-ahead short-term load forecasting

Accurate prediction of the power load is one of the keys to guarantee stable operation of power construction. However, with the surge of power load, the uncertainty of multi-step prediction brings more challenges. To reduce the uncertainty of multi-step short-term power load forecasting, a short-term ensemble day-ahead forecasting model based on variational modal decomposition (VMD) and two-group multi-step strategy is proposed in this paper. Firstly, VMD is used to decompose the original sequence into different sub-sequences. Then, according to the curve characteristics of different sub-sequences, different multi-step strategies are selected to achieve the best prediction effect. Finally, the multi-step results of all sub-sequences are integrated to get the final day-ahead prediction results. Among them, the two groups of multi-step strategies are divided into single-output approaches (Direct, Recursive, DirRec) and multi-output approaches (MIMO, RecMO, DirMO). For the prediction tool of the single-output approaches, a hybrid prediction model with integrating decomposition, feature selection, optimization algorithm and LSSVR is proposed. For the prediction tool of the multi-output approaches, a hybrid algorithm based on multi-kernel RMLSSVR is proposed to enhance the diversity of MLSSVR, and the parameters and weights of multi-kernel MLSSVR are optimized by the PSO with extreme value disturbance without velocity update. Taking the hourly market load data of California as an example, three groups of comparative experiments are carried out on the proposed method. Experiments show that the accuracy of our model is improved by at least 19% compared with the best RecMO strategy.