基于LSTM的超短期光伏发电功率预测

LSTM-based Ultra Short-term Photovoltaic Power Prediction

在当前环境下,光伏发电在能源供给中所占的比例越来越高,而对光伏发电厂的发电功率进行预测对电力系统调度十分重要。因此需要研究光伏功率预测,以便调度部门提前做好调度计划和风险规避。光伏发电功率对许多环境参数高度敏感,目前光伏发电功率预测大多仅围绕气象条件和历史数据建模,忽略了光伏设备本身性能与工作状态对发电功率的影响。为了进一步提高超短期光伏功率预测的预测精度,笔者针对多种可能影响光伏发电功率的数据进行建模,并进行仿真实验,结果表明本方法具有较高的精度,能够有效预测超短期光伏发电功率。

In the current environment,the proportion of photovoltaic power generation in the energy supply is getting higher and higher,and forecasting the power generation of photovoltaic power plants is very important for power system scheduling,so researching photovoltaic power forecasting helps dispatching departments to do well in advance Scheduling plans and risk aversion.Photovoltaic power generation is highly sensitive to many environmental parameters.At present,most photovoltaic power generation predictions are only modeled around meteorological conditions and historical data,ignoring the impact of the performance and working state of photovoltaic equipment on the power generation.In order to further improve the prediction accuracy of ultra-short-term photovoltaic power prediction,the operation status of the photovoltaic panel itself is combined with meteorological data to model,and simulation experiments are performed to verify the accuracy of the algorithm.