引用本文: | 程志友,余国晓,丁柏宏.采用改进温湿度变量策略的夏季短期负荷预测方法[J].电力系统保护与控制,2020,48(1):48-54.[点击复制] |
CHENG Zhiyou,YU Guoxiao,DING Baihong.Summer short-term load forecasting method based on improved temperature and humidity variable strategy[J].Power System Protection and Control,2020,48(1):48-54[点击复制] |
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摘要: |
为了充分考虑温度和湿度变量对夏季电力负荷的综合影响,提出一种改进的基于温湿度多形式变量的夏季短期负荷预测方法。首先通过分析夏季气象因素对负荷变化的影响,构造了三种不同形式的温湿度变量作为模型输入变量。然后根据周特性变化对负荷进行分层,对各层负荷建立基于LASSO回归的预测模型,并通过枚举搜索求解算法对输入变量进行选择,优化预测模型。最后通过计算剩余变量对应的系数从而进一步估计出各时段负荷的分布。算例结果表明该方法能有效提高模型的预测精度及鲁棒性。 |
关键词: 温湿度多形式变量 LASSO回归 枚举搜索求解 短期负荷预测 |
DOI:10.19783/j.cnki.pspc.190205 |
投稿时间:2019-02-26修订日期:2019-05-15 |
基金项目:国家自然科学基金资助项目资助(61672032);安徽省科技重大专项(18030901018) |
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Summer short-term load forecasting method based on improved temperature and humidity variable strategy |
CHENG Zhiyou,YU Guoxiao,DING Baihong |
(College of Electronics and Information Engineering, Anhui University, Hefei 230601, China;Power Quality Engineering Research Center, Ministry of Education, Hefei 230601, China) |
Abstract: |
In order to fully consider the combined effects of temperature and humidity variables on summer power load, an improved summer short-term load forecasting method based on temperature and humidity multi-form variables is proposed. Firstly, by analyzing the influence of summer meteorological factors on load changes, three different forms of temperature and humidity variables are constructed as model input variables. Then, the load is layered according to the change of the weekly characteristics, the prediction model based on LASSO regression is established for each layer load, and the input variables are selected by the enumeration search algorithm to optimize the prediction model. Finally, by calculating the coefficients corresponding to the residual variables, the load distribution in each period is further estimated. The example results show that the proposed method can effectively improve the prediction accuracy and robustness of the model. This work is supported by National Natural Science Foundation of China (No. 61672032) and Anhui Science and Technology Major Project (No. 18030901018). |
Key words: temperature and humidity multi-form variables LASSO regression enumeration search solution short-term load forecasting |