基于天气状态模式识别的光伏电站发电功率分类预测方法

A Classified Forecasting Approach of Power Generation for Photovoltaic Plants Based on Weather Condition Pattern Recognition

光伏发电功率的准确预测对电网调度的计划安排及光伏电站的优化运行具有重要意义。采用单一模型实现多种不同天气状态下光伏发电功率的准确预测非常困难。在分析辐照度变化规律基础上,综合考虑分类总数、类型代表性和分布均衡性,针对气象专业天气类型进行归纳合并,得到4种广义天气类型;进而给出光伏发电功率分类预测的基本框架;提取辐照度的特征参数,建立基于支持向量机的天气状态模式识别模型,辨识恢复部分历史数据所缺失的天气类型信息;最后利用光伏电站的实际运行数据进行仿真,结果验证了模式识别的准确性和分类预测的有效性。

Accurate photovoltaic （PV） power forecasting is very important for the dispatching of power grid and optimal operation of PV plants. It is very difficult for the unified model to forecast the output power of PV plant precisely under multiple weather conditions. Taken the total classification number, representativeness and equilibrium of distribution into consideration based on the analysis of variation law of solar irradiance, four generalized weather types were obtained by summarizing the meteorological professional weather types. The classification forecast approach of PV power was proposed subsequently. A weather status pattern recognition model based on support vector machine （SVM） was constructed with the input feature parameters extracted from solar irradiance data to identify the missing weather type label of part historical data. The accuracy of weather status pattern recognition and the validity of classification power forecast approach for PV plant are verified by the simulation using actual operating data of PV plant.