Multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

Article type: Research Article

Authors: Zhang, Lan^* | Xu, Lei

Affiliations: Xi’an Aeronautical Polytechnic Institute, Xi’an, Shaanxi, China

Correspondence: [*] Corresponding author: Lan Zhang, Xi’an Aeronautical Polytechnic Institute, Xi’an, Shaanxi, China. E-mail: [email protected].

Abstract: The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

Keywords: Multi-objective optimization problem, diagonal recursive neural network, quantum particle swarm optimization algorithm, load forecast

DOI: 10.3233/JCM-204736

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 21, no. 5, pp. 1113-1124, 2021