Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system

• Published in: Energy (Oxford) Vol. 148; pp. 59 - 78
• Main Authors: Wang, Jianzhou, Yang, Wendong, Du, Pei, Li, Yifan
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.04.2018; Elsevier BV
• Subjects: Algorithms; Data processing; Economic development; Economic forecasting; Electric power; Electric power grids; Electric power systems; Electrical loads; Electrical power system; Electricity distribution; Forecasting; Forecasting accuracy and stability; Hybrid forecasting framework; Mathematical models; Modules; Multi-objective optimization algorithm; Multiple objective analysis; Neural networks; Optimization; planning; Preprocessing; Social change; Stability; Wind speed; Electrical power system; Multi-objective optimization algorithm; Forecasting accuracy and stability; Hybrid forecasting framework
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2018.01.112

Electrical power system (EPS) forecasting plays a significant role in economic and social development but it remains an extremely challenging task. Because of its significance, relevant studies on EPS are especially needed. More specifically, only a few of the previous studies in this area conducted in-depth investigations of the entire EPS forecasting and merely focused on modeling individual signals centered on wind speed or electrical load. Moreover, most of these past studies concentrated on accuracy improvements and usually ignore the significance of forecasting stability. Therefore, to simultaneously achieve high accuracy and dependable stability, a hybrid forecasting framework based on the multi-objective dragonfly algorithm (MODA) was successfully developed in this study. The framework consists of four modules—data preprocessing, optimization, forecasting, and evaluation modules. In this framework, MODA is employed to optimize the Elman neural network (ENN) model as a part of the optimization module to overcome the drawbacks of single-objective optimization algorithms. In addition, data preprocessing and evaluation modules are incorporated to improve forecasting performance and conduct a comprehensive evaluation for this framework, respectively. Empirical results reveal that the developed forecasting framework can be an effective tool for the planning and management of power grids. •A novel forecasting framework is developed for electrical power system.•Propose a modified Elman neural network based on multi-objective optimization.•The accuracy and stability of developed framework are improved simultaneously.•The results are validated well in a whole electrical power system.