基于MIE-LSTM的短期光伏功率预测

提升精细化的光伏预测技术对电力系统的实时调度运行至关重要。它不仅依赖于预测模型的优劣,还依赖于训练样本日与预测日的相似程度。提出一种基于MIE-LSTM的短期光伏功率预测方法。在建立基于互信息熵(Mutual Information Entropy, MIE)的相关性衡量指标基础上,计算出光伏功率与各气象因素间的互信息熵,从而对高维气象数据进行降维处理。然后,利用历史日与预测日多维气象因素间的加权互信息熵筛选出相似日样本。最后,通过长短期记忆(Long-short Term Memory, LSTM)神经网络预测模型训练并建立气象因素与光伏出力之间的映射关系。通过对某实测光伏电站不同天气类型下的发电功率进行预测分析,验证了新方法能够达到理想的预测精度。

Short-term photovoltaic power forecasting based on MIE-LSTM

It is crucial for the real-time scheduling operation of the power system to improve the refined photovoltatic power forecasting technology. It depends not only on the superiority and inferiority of the predictive model, but also relies on the similarity between the training sample day and the forecast day. A novel method based on MIE-LSTM is proposed for short-term photovoltaic power forecasting. The correlation metrics is established based on Mutual Information Entropy (MIE), the MIE between photovoltaic power and meteorological factors is calculated to reduce the dimension of high-dimensional meteorological data. Then, the weighted mutual information entropy between the multi-dimensional meteorological factors of historical day and those of predicted day is used to screen out similar day samples. Finally, the mapping relationship between meteorological factors and photovoltaic output is established via training Long-Short Term Memory (LSTM) neural network prediction model. Through forecasting and analyzing the power generation of a photovoltaic power station under different weather types, it is verified that the new combination method can achieve ideal forecasting precision. This work is supported by National Natural Science Foundation of China (No. 51607009), Beijing Natural Science Foundation (No. 3172015), and Scientific Research Project of Beijing Education Council (No. KM201911232016).