Empirical Mode Decomposition based Multi-objective Deep Belief Network for short-term power load forecasting - Fan, Chaodong; Ding, Changkun; Zheng, Jinhua; Xiao, Leyi; Ai, Zhaoyang (Neurocomputing (Amsterdam), 2020)
With the rapid development of power grid data, the data generated by the operation of the power system is increasingly complex, and the amount of data increases exponentially. In order to fully exploit and utilize the deep relationship between data to achieve accurate prediction of power load, this paper proposes an Empirical Mode Decomposition Based Multi-objective Deep Belief Network prediction method (EMD-MODBN). In the training process of DBN, a multi-objective optimization model is constructed aiming at accuracy and diversity, and MOEA/D is used to optimize the parameters of the model to enhance the generalization ability of the prediction model. Finally, the final load forecasting results are obtained by summing up the weighted outputs of each forecasting model with ensemble learning method. The experimental results show that compared with several current better load forecasting methods, this method has obvious advantages in prediction accuracy and generalization ability.