| 引用本文: | 席雅雯,吴俊勇,石琛,朱孝文,蔡蓉.融合历史数据和实时影响因素的精细化负荷预测[J].电力系统保护与控制,2019,47(1):80-87.[点击复制] |
| XI Yawen,WU Junyong,SHI Chen,ZHU Xiaowen,CAI Rong.A refined load forecasting based on historical data and real-time influencing factors[J].Power System Protection and Control,2019,47(1):80-87[点击复制] |
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| 摘要: |
| 随着智能电网技术的飞速发展，对负荷预测的精度提出了越来越高的要求。融合负荷、天气等多源数据，提出了一种基于数据融合的支持向量机精细化负荷预测方法。首先对负荷历史数据进行聚类分析，将运行日分成六类。然后将负荷数据和温度、湿度等天气数据进行融合，针对六类聚类结果分别建立基于数据融合的支持向量机精细化负荷预测模型，并对模型参数进行全局优化。采用不同的预测模型对浙江省某地级市2013年的负荷进行预测，结果表明所提出的负荷预测方法的预测精度明显高于传统的负荷预测方法的预测精度。 |
| 关键词: 负荷预测 数据融合 支持向量机 预测精度 |
| DOI：10.7667/PSPC180050 |
| 投稿时间：2018-01-10修订日期：2018-03-13 |
| 基金项目:国家自然科学基金项目资助(51577009);ABB中国研究院项目资助(ABB20171128REU-CTR) |
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| A refined load forecasting based on historical data and real-time influencing factors |
| XI Yawen,WU Junyong,SHI Chen,ZHU Xiaowen,CAI Rong |
| (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China;ABB China Research Institute, Beijing 100015, China) |
| Abstract: |
| With the rapid development of smart grid technology, increasingly demand on the accuracy of load forecasting is put forward. Integrating load, weather and other multi-sourced data, a refined load forecasting method of Support Vector Machine (SVM) based on data fusion is proposed. Firstly, the historical load data is clustered and the operation days are divided into six categories. Then the weather data such as temperature and humidity are combined with the load data, and the refined load forecasting models of SVM based on data fusion are established respectively for the six clustering results. And the parameters of the model are optimized globally. Different forecasting models are used to predict the load of a prefecture-level city in Zhejiang Province in 2013, the prediction results show that the prediction accuracy of the load forecasting method proposed in this paper is obviously higher than that of the traditional load forecasting method. This work is supported by National Natural Science Foundation of China (No. 51577009) and ABB China Research Institute (No. ABB20171128REU-CTR). |
| Key words: load forecasting data fusion support vector machines (SVM) prediction accuracy |
