中国电力2024，Vol.57Issue(1)：9-17,9.DOI:10.11930/j.issn.1004-9649.202307100
面向虚拟电厂运营的温度敏感负荷分析与演变趋势研判
Analysis and Evolution Trend of Temperature-Sensitive Loads for Virtual Power Plant Operation
摘要
Abstract
With the frequent occurrence of extreme weather,the electricity consumption of temperature-sensitive loads is increasing year by year.As a high-quality regulation resource of virtual power plant(VPP),temperature-sensitive loads urgently need to be analyzed for the impact of meteorological changes on them.Due to the influence of abnormal weather such as extreme high temperature and large-scale cold waves,temperature-sensitive loads fluctuate violently.Conventional analysis and prediction methods are not adaptable to the extreme meteorological scenarios.Aiming at the problem of insufficient sample data and prediction accuracy of temperature-sensitive loads under cold wave weather,this paper proposes a daily maximum load prediction method for temperature-sensitive loads under the condition of small sample in cold wave weather.In this method,the TimeGAN is used to expand the small sample data during the cold wave period,and then the CNN-LSTM network is used to predict the daily maximum load during the cold wave period.Finally,the model is verified by the load data of a province in China during the winter period in the past two years.The results show that the prediction results of the proposed model are better than those of other models,with the prediction accuracy of the daily maximum load on the verification set being 99.5%.关键词
温度敏感负荷预测/寒潮/时间序列生成对抗网络/虚拟电厂/卷积神经网络/长短时记忆神经网络Key words
temperature sensitive load forecasting/cold wave/time series generative adversarial network/virtual power plant/convolutional neural network/long short-term memory neural network引用本文复制引用
周颖,白雪峰,王阳,邱敏,孙冲,武亚杰,李彬..面向虚拟电厂运营的温度敏感负荷分析与演变趋势研判[J].中国电力,2024,57(1):9-17,9.基金项目
国家电网有限公司科技项目(支撑重过载台区治理的区域供用电综合预测与智能预警技术研究与应用,5108-202218280A-2-379-XG).This work is supported by Science and Technology Project of SGCC(Regional Power Supply and Consumption Comprehensive Pre-Treatment Supporting Heavy Overload Station Area Governance,No.5108-202218280A-2-379-XG). （支撑重过载台区治理的区域供用电综合预测与智能预警技术研究与应用,5108-202218280A-2-379-XG）
