Affiliations: [a]
College of Automation and Electronic Engineering, Xiangtan University & Hunan Engineering Research Center of Multi-energy Cooperative Control Technology, Xiangtan, Hunan, China
| [b]
School of Computer Science and Technology, Hainan University, Hainan, China
| [c]
College of Electrical and Information Engineering, Hunan University, Changsha, China
Abstract: Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting.
Keywords: Short-term residential load forecasting, gate recurrent unit, multi-objective
optimization algorithm, deep learning
