Penghui Liu1,*, Tianyu Yang1, Peng Zhang2, Peiyuan Zou3
Energy Engineering, Vol.122, No.4, pp. 1387-1402, 2025, DOI:10.32604/ee.2025.062627
- 31 March 2025
Abstract The photovoltaic (PV) output process is inherently complex, often disrupted by a multitude of meteorological factors, while conventional detection methods at PV power stations prove inadequate, compromising prediction accuracy. To address this challenge, this paper introduces a power prediction method that leverages modal switching (MS), weight factor adjustment (WFA), and parallel long short-term memory (PALSTM). Initially, historical PV power station data is categorized into distinct modes based on global horizontal irradiance and converted solar angles. Correlation analysis is then employed to evaluate the impact of various meteorological factors on PV power, selecting those with strong… More >
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