摘要
精确的清洁能源发电功率预测是实现其高效利用的关键,然而,多种因素导致清洁能源发电功率波动剧烈,给功率预测带来巨大挑战。针对当前清洁能源大规模并网的需求,本文从多个角度探讨清洁能源发电功率预测模型的研究意义及其分类,对人工智能技术在清洁能源发电功率预测领域的最新应用,包括传统机器学习、深度学习和组合方法进行对比和总结。此外,深入分析影响清洁能源接入的关键因素,探讨清洁能源消纳分析模型的技术发展,阐述与预测模型不同的侧重点和发展方向。最后,展望了清洁能源预测与消纳分析模型的未来发展趋势及其日益展现的重要意义。
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.
作者
程远林
余虎
张舒
廖兴炜
张骁
张毅
刘昌会
CHENG Yuanlin;YU Hu;ZHANG Shu;LIAO Xingwei;ZHANG Xiao;ZHANG Yi;LIU Changhui(China Energy Engineering Group Hunan Electric Power Designing Institute Co.,Ltd.,Changsha Hunan 410007,China;School of Low-Carbon Energy and Power Engineering,China University of Mining&Technology,Xuzhou Jiangsu 221116,China)
出处
《广西师范大学学报(自然科学版)》
北大核心
2025年第5期1-15,共15页
Journal of Guangxi Normal University:Natural Science Edition
基金
国家自然科学基金(51906252)
江苏省自然科学基金(BK20190632)
中国博士后科学基金(2019M661980)
中国电力工程顾问集团有限公司重大科技专项(DG3-F03-2023)。
关键词
清洁能源
功率预测
预测模型
消纳
双碳目标
clean energy
power predicting
model prediction
consumption
double-carbon goal
