一种二模态天气分型方法及其在光伏功率概率预测的应用OACSTPCD
A Two-Modal Weather Classification Method and Its Application in Photovoltaic Power Probability Prediction
天气分型是光伏功率预测中不可或缺的预处理步骤,为精细刻画光伏出力的不确定性,提出一种新的基于光伏功率聚类的二模态天气分类方法.该方法结合气象信息和功率信息进行天气分型,为天气分型在光伏功率预测的应用提供了一条有效的新路径.此外,该方法使用数据融合技术,依据融合数值天气预报(numeric weather prediction,NWP)气象和实际气象二者间的相关信息进行天气分型,以减少模型对NWP准确度的依赖并提高模型的鲁棒性.以吉林某光伏电站数据为例,验证了该天气分型方法的合理性,同时,将天气分型方法与功率概率预测相结合,其测算结果表明,使用所提方法进行天气分型概率预测的区间覆盖率更接近预设的置信水平,且平均带宽更窄.
Weather classification is an indispensable preprocessing step in photovoltaic power prediction.A new photovoltaic power clustering based two-modal weather classification method was proposed to finely depict the uncertainty of photovoltaic power.Both photovoltaic power data and meteorological data were considered for weather classification,which provided a novel and effective path for weather classification based photovoltaic power prediction.In addition,data fusion technology was used to mine relevant information between numeric weather prediction(NWP)data and measured meteorological data to help for weather classification,so as to reduce the model reliance on the accuracy of forecasted meteorological indicators as well as improve the robustness of the model.In experiments based on the data of a photovoltaic power station in Jilin,the rationality of the weather classification method was demonstrated.The photovoltaic power probability prediction combined with the proposed weather classifier resulted in the prediction interval coverage probability closer to the preassigned confidence level,and a narrower mean prediction interval width.
付小标;李德鑫;侯嘉琪;李宝聚;温亚坤;赖晓文;郭雷;王志伟;王尧;张海锋
国网吉林省电力有限公司电力调度控制中心,吉林省 长春市 130021国网吉林省电力有限公司电力科学研究院,吉林省 长春市 130021北京清能互联科技有限公司创新中心,北京市 海淀区 100084清华四川能源互联网研究院交易与运筹研究所,四川省 成都市 610299
能源与动力
光伏发电天气分型光伏功率概率预测时间序列K均值聚类多模态学习不确定性
photovoltaic power generationweather classificationphotovoltaic power probability predictiontime series K-means clusteringmulti-modal learninguncertainty
《发电技术》 2024 (002)
299-311 / 13
国网吉林省电力有限公司揭榜挂帅项目(2021JBGS-09). Project Supported by Science and Technology Project Selected by the Open Competition Mechanism of State Grid Jilinsheng Electric Power Supply Company(2021JBGS-09).
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