A Two-Modal Weather Classification Method and Its Application in Photovoltaic Power Probability Prediction

FU Xiaobiao; HOU Jiaqi; LI Baoju; WEN Yakun; LAI Xiaowen; GUO Lei; WANG Zhiwei; WANG Yao; ZHANG Haifeng; LI Dexin

doi:10.12096/j.2096-4528.pgt.23017

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A Two-Modal Weather Classification Method and Its Application in Photovoltaic Power Probability Prediction

New Energy | 更新时间：2025-06-15

- A Two-Modal Weather Classification Method and Its Application in Photovoltaic Power Probability Prediction
- Power Generation Technology Vol. 45, Issue 2, Pages: 299-311(2024)
- 作者机构：
  
  1.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
  2.北京清能互联科技有限公司创新中心,北京市海淀区 100084
  3.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
  4.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
- 作者简介：
- 基金信息：
  
  Science and Technology Project Selected by the Open Competition Mechanism of State Grid Jilinsheng Electric Power Supply Company(2021JBGS-09)
- DOI：10.12096/j.2096-4528.pgt.23017
  CLC： TK 01;TM 615
- Received：20 February 2023，
  
  Published：30 April 2024
- 稿件说明：
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付小标,侯嘉琪,李宝聚等.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[J].发电技术,2024,45(02):299-311.

FU Xiaobiao,HOU Jiaqi,LI Baoju,et al.A Two-Modal Weather Classification Method and Its Application in Photovoltaic Power Probability Prediction[J].Power Generation Technology,2024,45(02):299-311.
付小标,侯嘉琪,李宝聚等.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[J].发电技术,2024,45(02):299-311. DOI： 10.12096/j.2096-4528.pgt.23017.

FU Xiaobiao,HOU Jiaqi,LI Baoju,et al.A Two-Modal Weather Classification Method and Its Application in Photovoltaic Power Probability Prediction[J].Power Generation Technology,2024,45(02):299-311. DOI： 10.12096/j.2096-4528.pgt.23017.

摘要

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。

Abstract

Weather classification is an indispensable preprocessing step in photovoltaic (PV) power prediction. A new two-modal weather classification methods based on PV power clustering was proposed to finely depict the uncertainty of PV power output. Both PV power data and meteorological data were considered for weather classification

providing a novel and effective path for PV power prediction. In addition

data fusion technology was used to extract relevant information from both numeric weather prediction (NWP) data and measured meteorological data to help for weather classification. This approach reduces the model’s reliance on the accuracy of forecasted meteorological indicators and improve the robustness of the model. Experiments based on data from a PV power station in Jilin demonstrated the rationality of the proposed weather classification method. Combining the PV power probability prediction with the proposed weather classifier resulted in prediction interval coverage probabilities closer to the preassigned confidence level and narrower mean prediction interval width.

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