光伏发电功率预测方法综述
Reviews of Photovoltaic Power Prediction Methods
蔡源 1吴浩 1唐丹1
作者信息
- 1. 四川轻化工大学自动化与信息工程学院,四川 宜宾 644000;人工智能四川省重点实验室,四川 宜宾 644000
- 折叠
摘要
精确的光伏发电功率预测是实现光伏电站顺利并网的关键.然而,太阳辐射、气候和地理条件等因素会导致光伏发电功率频繁波动,给功率预测带来了巨大挑战.针对当前光伏新能源大规模并网的需求,从多个角度探讨了光伏发电功率预测的意义及其分类,综述了人工智能技术在光伏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,并进行了对比和总结.目前研究的主要类型是单一光伏电站的超短期和短期光伏发电功率预测,深度学习方法和组合方法是主流预测方法,数据预处理、特征提取和误差补偿是提升预测精度的关键因素.最后,展望了人工智能技术在光伏发电功率预测领域的未来趋势和研究创新点.
Abstract
Accurate photovoltaic(PV)power prediction is the key to successful grid integration of PV power plants.However,factors such as solar radiation,climate and geographical conditions can cause frequent fluctuations in PV power generation,posing significant challenges to power prediction.In response to the current demand for large-scale grid integration of PV renewable energy,the significance and classification of PV power prediction are discussed from multiple perspectives.The latest applications of artificial intelligence(AI)technology in the field of PV power prediction are reviewed,including traditional machine learning,deep learning and hybrid methods,and are compared and summarized.Currently,the main types of researches are ultra-short-term and short-term PV power prediction for single PV power stations,and deep learning and hybrid methods are the mainstream prediction methods.Data pre-processing,feature extraction and error compensation are the key factors to improve prediction accuracy.Finally,future trends and research innovations in AI technology for PV power prediction are discussed.
关键词
光伏发电/机器学习/深度学习/功率预测/人工智能技术Key words
photovoltaic power generation/machine learning/deep learning/power prediction/artificial intelligence technology引用本文复制引用
基金项目
四川省科技厅项目(2022YFS0518)
四川省科技厅项目(2022ZHCG0035)
四川轻化工大学研究生创新基金(Y2023294)
出版年
2024
NETL
NSTL
万方数据