| 引用本文: | 徐冰涵,孙云莲,易仕敏,等.考虑分时电价的居民用户短期用电量分类预测及修正方法[J].电力系统保护与控制,2020,48(6):144-151.[点击复制] |
| XU Binghan,SUN Yunlian,YI Shimin,et al.Classified short-term electricity consumption forecasting and correcting method for residential users considering time-of-use electricity price[J].Power System Protection and Control,2020,48(6):144-151[点击复制] |
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| 摘要: |
| 为了更好地利用智能电网中的用户用电信息，提高短期用电量预测精度，针对居民用户提出一种考虑分时电价的分类短期用电量预测及修正方法。首先，通过模糊聚类将用户按用电行为分类，将电价、用电量和加权气象日期影响因素作为预测模型输入量。然后，针对各类用户的用电特点，经仿真对比选择相适应的BP、Elman、LSTM神经网络算法构建预测模型。最后，运用修正算法对误差较大的峰谷值进行修正，将修正后的分类预测结果相加以获得整体预测值。以广东省云浮市某小区为例对该方法进行仿真分析，并与随机森林、CART等算法进行对比。实验结果证明所提方法具有更高的预测精度。 |
| 关键词: 用电量预测 分时电价 模糊聚类 神经网络 修正 |
| DOI：10.19783/j.cnki.pspc.190485 |
| 投稿时间：2019-04-30修订日期：2019-08-25 |
| 基金项目:南方电网公司科技项目资助(035300KK52150007，031800KK52170073) |
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| Classified short-term electricity consumption forecasting and correcting method for residential users considering time-of-use electricity price |
| XU Binghan,SUN Yunlian,YI Shimin,WANG Huayou,XIE Wenwang |
| (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China;Guangdong Power Grid Co., Ltd., Guangzhou 510620, China) |
| Abstract: |
| In order to make better use of the user's electricity consumption information in the smart grid and improve the forecasting accuracy, a classified short-term electricity consumption forecasting and correcting method for residential users considering time-of-use electricity price is proposed. Firstly, the fuzzy clustering is used to classify the users according to their electricity usage behavior, and the electricity price, electricity consumption and weighted weather and date influence factors are used as the predictive model input. Secondly, according to the simulation comparison, the BP, Elman and LSTM neural network algorithms are chosen for corresponding users to construct the prediction model. Finally, the modified algorithm is used to correct the peak-to-valley value with large error, and the corrected classification prediction results are added up to obtain the overall predicted value. Taking a certain community in Yunfu City, Guangdong Province as an example, the method is simulated and compared with random forest and CART algorithms. Experimental results show that the proposed method has higher prediction accuracy. This work is supported by Science and Technology Project of China Southern Power Grid Company (No. 035300KK 52150007 and No. 031800KK52170073). |
| Key words: electricity consumption forecasting time-of-use electricity price fuzzy clustering neural networks correction |
