基于支持向量回归预测模型考虑天气因素和分时电价因素的短期电力负荷预测
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引用本文:魏步晗,鲍刚,李振华.基于支持向量回归预测模型考虑天气因素和分时电价因素的短期电力负荷预测[J].电网与清洁能源,2023,39(11):9~19
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作者单位
魏步晗 三峡大学电气与新能源学院 
鲍刚 三峡大学电气与新能源学院 
李振华 三峡大学电气与新能源学院 
基金项目:国家自然科学基金项目(52277012)
中文摘要:为了保证电力系统安全稳定的运行,短期负荷预测在电力系统调度中越来越重要。提出了一种基于支持向量回归(support vector regression,SVR)的短期负荷预测模型,考虑了天气因素和分时电价因素对负荷的影响。研究分析了2019年1月1日—2022年1月1中国江苏省某地区的日负荷特征;基于天气因素、考虑分时电价因素,建立了SVR模型,通过SVR模型对历史负荷数据进行训练并对未来负荷进行预测。实验结果表明,所提模型在短期负荷预测方面具有较高的准确性;基于天气因素考虑分时电价因素对负荷的影响,能够更好地适应不同天气条件下和不同电价下的负荷需求。
中文关键词:负荷预测  天气因素  分时电价因素  支持向量回归
 
Short-Term Electricity Load Forecasting Based on Support Vector Regression Forecasting Model Considering Weather Factors and Time-of-Use Tariff Factors
Abstract:In this paper, we propose a short-term load forecasting model based on Support Vector Regression (SVR), which takes into account the effects of weather factors and time-of-use tariff factors on load. To begin with, we studied and analyzed the daily load characteristics from January 1, 2019 to January 1, 2022 in an area of Jiangsu Province, China. Furthermore, based on the weather factor and considering the time-of-use tariff factor, the SVR model was established, and the SVR model was used for training and forecasting. The experimental results show that the model proposed in this paper has high accuracy in short-term load forecasting, and it can better adapt to the load demand under different weather conditions and different electricity prices based on the weather factor and considering the impact of time-of-use tariff factors on the load.
keywords:load forecasting  weather factors  time-of-use tariff factors  support vector regression
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