RECENT PROGRESS OF SHORT-TERM FORECASTING OF PHOTOVOLTAIC GENERATION BASED ON ARTIFICIAL NEURAL NETWORKS

Jia Lingyun; Yun Sining; Zhao Zeni; Li Honglian; Wang Shangyu; Yang Liu

doi:10.19912/j.0254-0096.tynxb.2021-0501

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (12) : 88-97. DOI: 10.19912/j.0254-0096.tynxb.2021-0501

RECENT PROGRESS OF SHORT-TERM FORECASTING OF PHOTOVOLTAIC GENERATION BASED ON ARTIFICIAL NEURAL NETWORKS

Jia Lingyun¹, Yun Sining¹, Zhao Zeni¹, Li Honglian², Wang Shangyu³, Yang Liu³

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Abstract

Accurate short-term forecasting of photovoltaic generation is crucial to ensure power dispatching and large-scale photovoltaic grid connection. This review paper presents an extensive review on recent progress in the short-term forecasting of solar power generation. The correlation analysis of various meteorological factors affecting on solar power generation is implemented. The artificial neural network models and deep learning models for solar power forecasting are summarized and reviewed. The solar irradiance is the main meteorological parameter affecting the accuracy of forecasting models. In the short-term forecasting of solar power generation, both neural network models and hybrid models demonstrate a satisfactory prediction accuracy, whereas the hybrid models perform better than the single forecasting models in the prediction accuracy.

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photovoltaic power / neural networks / forecasting / deep learning / correlation

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Jia Lingyun, Yun Sining, Zhao Zeni, Li Honglian, Wang Shangyu, Yang Liu. RECENT PROGRESS OF SHORT-TERM FORECASTING OF PHOTOVOLTAIC GENERATION BASED ON ARTIFICIAL NEURAL NETWORKS[J]. Acta Energiae Solaris Sinica. 2022, 43(12): 88-97 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0501

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