Deterministic and Probabilistic Wind Power Forecasting Based on Bi-Level Convolutional Neural Network and Particle Swarm Optimization

Abstract

The intermittency and uncertainty of wind power result in challenges for large-scale wind power integration. Accurate wind power prediction is becoming increasingly important for power system planning and operation. In this paper, a probabilistic interval prediction method for wind power based on deep learning and particle swarm optimization (PSO) is proposed. Variational mode decomposition (VMD) and phase space reconstruction are used to pre-process the original wind power data to obtain additional details and uncover hidden information in the data. Subsequently, a bi-level convolutional neural network is used to learn nonlinear features in the pre-processed wind power data for wind power forecasting. PSO is used to determine the uncertainty of the point-based wind power prediction and to obtain the probabilistic prediction interval of the wind power. Wind power data from a Chinese wind farm and modeled wind power data provided by the United States Renewable Energy Laboratory are used to conduct extensive tests of the proposed method. The results show that the proposed method has competitive advantages for the point-based and probabilistic interval prediction of wind power.

Keywords

• wind power forecasting
• convolutional neural network
• variational mode decomposition
• phase space construction
• particle swarm optimization