清洁能源预测与消纳分析模型研究进展

doi:10.16088/j.issn.1001-6600.2024090601

Abstract

Abstract: Accurate power prediction of clean energy generation is the key to realize the efficient utilization of clean energy. However, a variety of factors lead to drastic fluctuations in clean energy power generation, which brings great challenges to power prediction. In view of the current demand for large-scale grid connection of clean energy, the research significance of clean energy power generation prediction models and their classification are discussed from multiple perspectives, and the latest applications of artificial intelligence technology in the field of clean energy power generation prediction, including traditional machine learning, deep learning and combinatorial methods, are reviewed, compared and summarized. In addition, the key factors affecting clean energy access are analyzed in depth, and the technical development of clean energy consumption analysis models is discussed, with different focuses and development directions from those of prediction models elaborated. Finally, the future development trend of clean energy prediction and consumption analysis models and their increasing significance are discussed.

Key words: clean energy, power predicting, model prediction, consumption, double-carbon goal

CLC Number: TK-9

CHENG Yuanlin, YU Hu, ZHANG Shu, LIAO Xingwei, ZHANG Xiao, ZHANG Yi, LIU Changhui. Recent Advances on the Study of Clean Energy Forecasting and Consumption Analysis Model[J].Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(5): 1-15.

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Recent Advances on the Study of Clean Energy Forecasting and Consumption Analysis Model

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