A novel interval forecasting system based on multi-objective optimization and hybrid data reconstruct strategy. (1st May 2023)

• Record Type: Journal Article
• Authors: Wang, Jianzhou; Zhou, Yilin; Jiang, He
• Is Part Of: Expert systems with applications. Volume 217(2023)
• Publication Date: 2023-05-01
• Subjects: Interval forecast -- Data reconstruct method -- Multi-objective optimization -- Photovoltaic power forecasting; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2023.119539
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel hybrid data reconstruct strategy is developed. Comprehensive and rational subsystem determining and training strategies are applied. A suitable interval forecasting framework was established successfully. A scientific and pluralistic evaluation system is constructed. Abstract: With the continuous increase in global photovoltaic installations, the importance of photovoltaic power generation to the power industry has gradually increased, which means that accurate forecasting and real-time management of photovoltaics have become indispensable. In recent years, although many photovoltaic prediction systems based on different mechanisms have been proposed, most of them are point prediction methods and do not fully consider the impact of various factors on photovoltaic power generation. Therefore, in order to will fill this gap, a novel hybrid interval prediction system that combines hybrid signal preprocessing technique, random forest algorithm, deep learning model, neural network model and swarm intelligence optimization strategy is designed in this paper. The system can make full use of the characteristics of independent variables, and effectively improve the stability and accuracy of photovoltaic prediction. According to the data obtained from Yulara Solar System, the prediction efficiency of the suggested system is verified. Specifically, when the interval width coefficient is 0.15, the prediction interval coverage probabilities obtained by the presented system are 73.373%, 92.899%, and 92.781%, respectively. Furthermore, this paper identifies the superior stability and application possibilities of the proposed interval forecasting system from multiple perspectives.