Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm
Leijiao Ge, Yuanliang Li, Jun Yan, Yuqian Wang, Na Zhang
Abstract
To improve energy efficiency and protect the environment, the integrated energy system (IES) becomes a significant direction of energy structure adjustment. This paper inno-vatively proposes a wavelet neural network (WNN) model optimized by the improved particle swarm optimization (IPSO) and chaos optimization algorithm (COA) for short-term load prediction of IES. The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models. First, the Pearson correlation coefficient is employed to select the key influencing factors of load prediction. Then, the traditional particle swarm optimization (PSO) is improved by the dynamic particle inertia weight. To jump out of the local optimum, the COA is employed to search for individual optimal particles in IPSO. In the iteration, the parameters of WNN are continually optimized by IPSO-COA. Meanwhile, the feedback link is added to the proposed model, where the output error is adopted to <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mo</sup> dify the prediction results. Finally, the proposed model is employed for load prediction. The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network (ANN), WNN, and PSO-WNN.