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文章摘要
基于气象大数据的城市电力负荷预测
Urban electricity consumption prediction based on meteorological big data
Received:May 06, 2019  Revised:May 06, 2019
DOI:10.19753/j.issn1001-1390.2021.02.014
中文关键词: 用电量预测  预测模型  时序卷积网络  循环神经网络  电力数据
英文关键词: electricity consumption forecasting  prediction model  temporal convolution network  recurrent neural network  power supply data
基金项目:国网上海市电力公司科技项目资助(项目编号:B3094018003U)
Author NameAffiliationE-mail
Wei Xiaochuan* Shanghai Electric Power Company wxc_sgcc@outlook.com 
Wang Xingang Shanghai Electric Power Company wxc_sgcc@outlook.com 
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中文摘要:
      为克服利用气象因素对用电量预测任务中必须先观测到气象条件再进行预测的困境,提升用电量预测准确性,提出一种基于时序卷积网络与循环神经网络的用电量预测方法。首先使用时序卷积网络基于历史气象数据对未来气象条件进行预测,再结合历史用电量数据对未来用电量数据进行预测。算法在预测当前用电量时只依赖于过去的特征,因此无需先观测到当前气象特征。在真实的气象与用电量数据集上的实验结果表明,在仅使用气象因素这一外部变量时,算法对用电量的预测准确性超出了传统方法,有较高的实用性。
英文摘要:
      To overcome the difficulty in electricity consumption forecasting based on meteorological data where observed present meteorological characters are necessary, and improve the accuracy of forecasting, an electricity consumption prediction method based on temporal convolution network (TCN) and recurrent neural network (RNN) was proposed. First, future weather condition is predicted based on historical meteorological data with TCN, and then future electricity consumption is predicted based on future weather condition and historical electricity consumption. During this process, the proposed method depends only on historical data, and without necessity of the observation of present weather condition. Experimental results on real-world meteorological dataset and electricity consumption dataset demonstrated that the proposed method showed an advantage in electricity consumption prediction task when weather is the only external factor used, and of high applicability.
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