基于无爬坡事件定义标准晴空集的短期光伏功率预测  

Short-Term Photovoltaic Power Prediction Based on Standard Clear Sky Set Defined by No Climbing Event

作　　者：郭洪武 车建峰 闫钇汛 王丽婕[4] GUO Hongwu;CHE Jianfeng;YAN Yixun;WANG Lijie(Chifeng Power Supply Company,State Grid Inner Mongolia East Electric Power Co..Ltd.,Chifeng 024000,China;China Electric Power Research Institute,Beijing 100192,China;China Railway Publishing House Company Limited,Beijing 100054,China;School of Automation,Beijing Information Science and Technology University,Beijing 100192,China)

机构地区：[1]国网内蒙古东部电力有限公司赤峰供电公司,内蒙古赤峰024000 [2]中国电力科学研究院有限公司,北京100192 [3]中国铁道出版社有限公司,北京100054 [4]北京信息科技大学自动化学院,北京100192

出　　处：《中国电力》2023年第9期187-195,共9页Electric Power

基　　金：国网内蒙古东部电力有限公司科技项目(SGMDCF00YWJS2000769)。

摘　　要：光伏功率的输出受季节、气象条件及其他因素的影响具有随机性和不确定性,恶劣天气下功率输出具有较强的波动性也加大了预测的难度。提出了一种基于无爬坡事件定义标准晴空集的短期光伏功率预测模型。通过爬坡定义提取一天内均为无爬坡事件的样本点,将其定义为一个标准晴空集,并与历史实际功率做差,得到的差值作为输出目标变量,以数值天气预报作为输入变量,采用长短期记忆模型对差值进行建模预测,最后将标准晴空集与该预测差值做差,间接得到预测的光伏输出功率值。通过对某光伏电站进行仿真,并进行算例对比,所提模型的短期光伏功率预测精度提高了2%~4%,在恶劣天气下,该方法可以将平均绝对误差和均方根误差降低3%左右,验证了所提模型的性能和有效性。Photovoltaic power output is influenced by season,weather conditions,and other factors with randomness and uncertainty.It is difficult to predict the power output under bad weather with strong volatility.Therefore,this paper proposed a short-term photovoltaic power prediction model based on the standard clear sky set with no climbing event definition.The sample points with no climbing events in one day were extracted by the climbing definition and defined as a standard clear sky set,and the difference between them and the historical actual power was made.The obtained difference was used as the output target variable,and the numerical weather forecast was used as the input variable.The long-short term memory(LSTM)model was used to model and forecast the difference.Finally,the predicted photovoltaic output power was obtained indirectly by making the difference between the standard clear sky set and the predicted difference.Through the simulation of a photovoltaic power station and comparison of calculation examples,the short-term photovoltaic power prediction accuracy of the proposed model was improved by 2%~4%.In severe weather,this method can reduce mean absolute error(MAE)and root-mean-square error(RMSE)by about 3%,which verifies the prediction performance and effectiveness of the proposed model.

关 键 词：光伏功率短期预测 非爬坡样本提取 标准晴空集 长短期记忆模型 

分 类 号：TM615[电气工程—电力系统及自动化]