基于云图特征提取的改进混合神经网络超短期光伏功率预测方法

An Improved Hybrid Neural Network Ultra-short-term Photovoltaic Power Forecasting Method Based on Cloud Image Feature Extraction

光伏功率时序受多种特征因素的影响,呈现出高度的随机性和波动性。不同于分布式光伏,集中式光伏具有地理位置与辐照水平的同一性,运动型云层的遮挡往往导致光伏功率的分钟级剧烈波动,对光伏功率预测精度提出了挑战。针对上述问题,该文提出基于云图特征提取的改进混合神经网络超短期光伏功率预测方法。首先,通过提取并匹配彩色云图局部特征描述子,提出基于地基云图的云轨迹跟踪方法;其次,为评估运动型云团引起的超短期辐照度变化,建立基于云轨迹追踪的辐照系数预测模型;为表征各特征序列的内在相关性,提出一种基于改进注意力机制(improved attentionmechanism,IAM)的卷积–长短时记忆混合神经网络(convolutional neural network-long and short-term memory network,CNN-LSTM)进行超短期光伏功率预测。在此基础上,综合天气类型与波动性聚类识别并提取功率波动过程,建立误差修正模型以进一步提高预测精度。采用西北某集中式光伏电站数据进行算例验证,结果表明,所提方法能有效提高预测精度,具有一定工程实用价值。

The photovoltaic power sequence is affected by a variety of characteristic factors,showing a high degree of randomness and volatility.Unlike distributed photovoltaics,centralized photovoltaics have the same geographic location and irradiation level.The occlusion of sporty clouds often leads to minute-level fluctuations in photovoltaic power,which poses a challenge to the accuracy of photovoltaic power prediction.To solve the above problems,an improved hybrid neural network ultra-short-term photovoltaic power prediction method was proposed in this paper,based on cloud image feature extraction.First,by extracting and matching local feature descriptors of color cloud images,a cloud trajectory tracking method based on ground-based cloud images was proposed;Secondly,in order to evaluate the ultra-short-term irradiance changes caused by the moving cloud clusters,an irradiance coefficient prediction model based on cloud trajectory tracking was established.Furthermore,in order to characterize the inherent correlation of each feature sequence,an IAM-CNN-LSTM hybrid neural network was proposed for ultra-short-term photovoltaic power prediction.On this basis,this paper combined weather type and volatility cluster identification,extracted the power fluctuation process and established an error correction model to further improve the prediction accuracy.The data of a centralized photovoltaic power station in Northwest China was used for verification.The results show that the method proposed in this paper can effectively improve the prediction accuracy and has certain engineering practical value.