基于自适应Kmeans和LSTM的短期光伏发电预测

光伏发电功率预测是可持续电力系统设计，能源转换管理和智能电网建设领域的重要主题。精准的光伏发电功率预测是电网日常调度管理与安全稳定运行的关键。本文提出了一种基于自适应Kmeans和长短期记忆(LSTM)的短期光伏发电功率预测模型。根据短期光伏发电特性，选取了预测模型的初始训练集。采用自适应Kmeans对初始训练集以及预测日的光伏发电功率进行聚类。在各类别的初始训练集数据上分别训练LSTM，结合训练完成的LSTM进行发电功率的预测。最后，考虑三种典型天气类型，采用所提方法进行仿真分析。结果表明，与其他三种方法相比，本文提出的方法的精度有了明显提升，误差更小。

Short-term photovoltaic power generation prediction based on adaptive Kmeans and LSTM

Photovoltaic power forecasting is an important topic in the fields of sustainable power system design, energy conversion management, and smart grid construction. Accurate prediction of photovoltaic power generation is the key to daily dispatch management, safe and stable operation of the power grid. Therefore, a short-term photovoltaic power generation prediction model based on adaptive Kmeans and long short-term memory (LSTM) is proposed. According to the short-term photovoltaic power generation characteristics, the initial training set of the prediction model is selected. The adaptive Kmeans is used to cluster the photovoltaic power generation of the initial training set and the prediction day. A LSTM is trained on the initial training set data of each category, and combining the trained LSTM to predict the power generation. Finally, considering three typical weather types, the proposed method is used for simulation analysis. The results show that, compared with the other three methods, the accuracy of the proposed method is improved significantly, and the error is smaller.