Review on the application of artificial intelligence in data mining and wind and photovoltaic power forecasting

doi:10.3969/j.issn.2097-0706.2025.03.004

Abstract

Abstract:

As global demand for renewable energy continues to surge， efficiently and intelligently managing and forecasting renewable energy generation has become a pivotal research objective in energy sector. Applications of artificial intelligence （AI） technologies in the multi-dimensional data processing and intelligent forecasting of renewable energy generation are explored， focusing on its role in handling complex and highly variable data. First，the role of multi-dimensional feature mining techniques in processing wind and solar energy generation data from the perspective of meteorological conditions and spatiotemporal features is studied. Subsequently， a systematic analysis on intelligent forecasting techniques applied across different spatiotemporal scales and scenarios is offered， with particular emphasis on its usage in machine learning and deep learning models. These models have gained significant attention for their outstanding performance in dealing with nonlinear and high-dimensional data. Thorough reviews on the latest research findings demonstrate the substantial benefits of these AI technologies in enhancing the accuracy and efficiency of wind and solar energy generation forecasts. Additionally， it delves into the strengths and limitations of existing technologies and their development directions， particularly emphasizing the importance of improving the robustness， real-time processing capabilities， and adaptability of intelligent forecasting models in various scenarios. This study provides theoretical insights and practical guidance for advancing the development of renewable energy.

Key words: AI, renewable energy generation, meteorological feature extraction, spatiotemporal feature extraction, photovoltaic power forecasting, wind power forecasting, data mining

CLC Number:

TK01

ZHANG Dongdong, SHAN Linke, LIU Tianhao. Review on the application of artificial intelligence in data mining and wind and photovoltaic power forecasting[J]. Integrated Intelligent Energy, 2025, 47(3): 32-46.

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方法类型	优点	缺点	准确度	计算时间	资源消耗	数据需求	可解释性
物理方法	依靠物理定律，准确性高；适合理解和解释物理现象；模型透明度高，易于调试	模型复杂度高，计算量大；需要高质量数据；难以处理复杂和非线性问题	高	中等	较低	中等	高
统计方法	适合处理大型数据集；可提供概率预测	难以捕捉数据中的复杂关系	中等	较短	较高	较少	中等
人工智能方法	能够处理复杂的非线性问题；自适应学习能力强，模型性能高；适合实时预测和大数据分析	模型训练时间长，计算资源消耗大；模型可解释性差；需要大量标注数据进行训练	高	长	低	多	较低
组合方法	综合多种方法的优势，提高预测精度，灵活性高；可适应不同场景要求	模型结构复杂，难以实施；需要协调不同方法之间的冲突；参数调整困难，开发周期长	较高	较长	中等	中等	中等

模型类型	适用场景	优点	缺点	适用时间尺度	适用空间尺度
传统机器学习模型	数据量较小且特征相对稳定的预测任务	训练速度快，模型简单且易解释	对复杂非线性关系和长期依赖关系的捕捉能力较弱	超短期预测、数据较少的中长期预测	单一机组
深度学习模型	数据量大且非线性关系复杂的预测任务	能捕捉复杂的非线性关系和长期依赖	需要大量数据和计算资源，容易过拟合，解释性较弱	短期预测和中长期预测（尤其是非线性和长期依赖显著的任务）	单一机组、单一电场、多电场集群
强化学习模型	动态变化环境中需要实时决策的预测任务	自适应性强，能够在动态环境中学习和优化策略	训练难度大，稳定性可能较差，调参复杂	超短期和短期预测（尤其是在动态环境中）	单一机组、单一电场
混合模型	需要整合不同模型优势的综合预测任务	能结合不同模型的优势，提高预测精度和鲁棒性	模型结构复杂，训练时间长，计算资源需求高	超短期到中长期预测（尤其是复杂场景下的预测任务）	单一机组、单一电场、多电场集群