Photovoltaic power combination prediction system based on improved multi‐objective optimization algorithm and nonlinear weighting strategy

Abstract

AbstractThe large‐scale connection of photovoltaic (PV) power generation to the power grid introduces considerable challenges to the grid's stable operation. To ensure grid stability, the key is to improve the accuracy of PV power prediction. Currently, PV power prediction is primarily dependent on single‐model construction, which ignores the role of data processing technologies, thus resulting in inadequate prediction performance. Accordingly, in this study, a combined prediction system is developed by integrating advanced deep learning algorithms and data processing techniques to improve the accuracy of PV power prediction. In addition, a nonlinear weighting strategy based on an improved multi‐objective dragonfly optimization algorithm (IMODA) is proposed to determine the final prediction result. In the IMODA, cloud model generator, tent mapping, and Pareto solution selection strategy based on knee points are introduced to resolve the defects of the original algorithm. The performance of the proposed combined system was scientifically evaluated and analysed by considering the PV power datasets of four seasons in Belgium as an example. The mean absolute percentage errors of the proposed system on the four datasets are 4.0198, 4.7943, 4.3587, and 5.9286. These values indicate an improvement range of 5%–30% compared with the errors of other benchmark models.