A Parallel Short-Term Power Load Forecasting Method Considering High-Level Elastic Loads
Jizhe Dong, Long Luo, Yu Lu, Qi Zhang
Abstract
This paper proposes an electric load forecasting model for systems containing high-level elastic loads. The model consists of three structures: a one-dimensional (1D) convolutional network structure, a parallel forecasting structure, and a deep residual network structure. The 1D convolutional network structure is utilized to extract features from the input data. The parallel forecasting structure is employed to predict the basic and elastic components of the loads. The deep residual network structure is designed to enhance the model’s generalization ability and prevent vanishing gradients. The proposed model is tested on the ISO-NE and Malaysia datasets, and the results demonstrate that the proposed model outperforms existing models and has high generalization ability.