整县光伏发电的综合LSTM功率预测模型研究

为了更好实现整县屋顶光伏集约化发展，提高大规模分布式屋顶光伏并网经济性，分析基于气象数据的预测方法弊端问题，挖掘海量时序光伏发电功率数据规律，提出基于改进长短期记忆递归神经网络(LSTM)的整县光伏综合功率预测模型。使用所提模型进行单一光伏电源预测实验，结果表明，改进LSTM模型较其他模型更精准，且在整县光伏综合功率预测场景下具有更高整体预测精度，可以用于整县光伏发电功率预测，有助于实现高比例分布式光伏消纳。

Study on Comprehensive LSTM Power Prediction Model of Photovoltaic Power Generation in whole County

In order to better realize the whole county photovoltaic roof intensive development, improve the economics of large-scale distributed photovoltaic grid roof ,analyze disadvantages of prediction method based on the meteorological data, explore the rule of the massive time-series photovoltaic power data , based on improved both short-term and long-term memory ,recursive neural network (LSTM) of the whole county comprehensive photovoltaic power prediction model is proposed. The model is used to predict a single photovoltaic power supply. The results show that the improved LSTM model is more accurate than other models, and it has higher overall prediction accuracy under the scenario of the whole county photovoltaic comprehensive power prediction. It can be used to predict the whole county photovoltaic power generation power, which is helpful to achieve a high proportion of distributed photovoltaic consumption.