基于多层次云图特征与宽度学习的超短期光伏功率预测

Ultra-short-term Photovoltaic Power Forecasting Based on Multi-level Sky Image Features and Broad Learning

针对现有研究云图信息利用不充分、爬坡功率预测误差较大而导致超短期光伏功率预测性能提升受限的问题,提出一种基于多层次云图特征与宽度学习的超短期光伏功率预测方法。首先,提取地基云图的多层次特征作为功率预测模型的图像特征量;同时,引入云层覆盖率与云层变化率作为爬坡识别模型的图像特征量。其次,结合历史功率数据,构建基于宽度学习的光伏功率预测模型与爬坡识别模型。最后,若爬坡识别结果为非爬坡事件,则直接应用功率预测模型得到预测结果;反之,则使用与爬坡事件相关的历史数据对功率预测模型进行增量更新,并基于更新后的功率预测模型得到预测结果。实验结果表明,所提出的方法能够有效提高超短期光伏功率的预测精度。

Aiming at the problems of insufficient use of sky image information and large errors in ramp power forecasting,which lead to the limited predictive performance improvement,an ultra-short-term photovoltaic power forecasting method based on multilevel sky image features and broad learning is proposed.Firstly,the multi-level features of the ground-based sky image are extracted as the image features of the power forecasting model.At the same time,the cloud coverage and cloud change rate are introduced as image features of the ramp recognition model.Secondly,combined with the historical power data,the photovoltaic power forecasting model and the ramp recognition model based on the broad learning are developed.Finally,if the ramp recognition result is a non-ramp event,the forecasting results are obtained according to the power forecasting model.However,if the ramp recognition result is a ramp event,the power forecasting model is incrementally updated using the historical data related to the ramp event,and the forecasting results are obtained based on the updated power forecasting model.The experimental results show that the proposed method can effectively improve the forecasting accuracy of ultra-short-term photovoltaic power.