| 引用本文: | 文贤馗,何明君,张俊玮,等.基于K均值聚类的光伏集群发电功率超短期预测研究[J].电力系统保护与控制,2025,53(12):165-172.[点击复制] |
| WEN Xiankui,HE Mingjun,ZHANG Junwei,et al.Research on ultra-short-term power forecasting of photovoltaic clusters based on K-means clustering[J].Power System Protection and Control,2025,53(12):165-172[点击复制] |
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| 摘要: |
| 准确的分布式光伏超短期功率预测对于分布式光伏接入电网具有至关重要的意义，但是当前分布式光伏功率预测中存在气象数据精度不够、功率数据不完整等问题。为此，提出了一种基于集群划分的区域光伏预测方法。首先选择正反向电量比、功率中位数与平均数比值两个维度作为距离计算依据，采用K均值聚类算法对区域中所有光伏电站进行集群划分。在集群划分的基础上，对每个集群分别进行光伏功率预测，然后综合所有集群的预测结果实现对分布式光伏区域预测。最后采用某区域分布式光伏发电场站数据进行了验证。结果表明：所提算法精度较高，所提方法能够满足现场的要求。 |
| 关键词: 分布式光伏 集群划分 功率预测 K均值聚类 长短期记忆网络 |
| DOI：10.19783/j.cnki.pspc.241078 |
| 投稿时间：2024-08-13修订日期：2025-01-11 |
| 基金项目:贵州省科技计划项目资助(CXTD[2022]008)；南方电网科技项目资助(GZKJXM20222258) |
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| Research on ultra-short-term power forecasting of photovoltaic clusters based on K-means clustering |
| WEN Xiankui1,HE Mingjun1,ZHANG Junwei1,ZHOU Ke1,CAI Yongxiang1,ZHANG Fan2 |
| (1. Guizhou Power Grid Electric Power Science Research Institute, Guiyang 550002, China;
2. CSG Artificial Intelligence Technology Co., Ltd., Guangzhou 510000, China) |
| Abstract: |
| Accurate ultra-short-term power forecasting for distributed photovoltaic (PV) systems is critically important for their integration into the power grid. However, current forecasting efforts face challenges such as insufficient accuracy in meteorological data and incomplete power data. To address these issues, this paper proposes a regional PV forecasting method based on cluster partitioning. First, two metrics, i.e., forward/reverse electricity ratio and the ratio of power median to mean, are selected as the basis for distance calculation. The K-means clustering algorithm is then used to divide all PV power plants in the region into clusters. Based on this clustering, PV power forecasting is carried out for each cluster, and the combined results from all clusters are used to produce a regional forecast for distributed PV output. Finally, data from a distributed PV power plant in a specific region is used for verification. The results show that the proposed algorithm has high accuracy and can meet the practical requirements of field applications. |
| Key words: distributed photovoltaics cluster partitioning power forecasting K-means clustering long short-term memory network |
