摘要
在新型电力系统中,用户用电行为具有复杂性和动态性,且大多来自多源数据,多样的数据序列导致用电负荷预测的精度低,影响预测结果.因此,提出新型电力系统用户侧用电负荷精准预测研究.首先,采集大量的用户侧用电负荷数据,并对这些数据进行聚类处理,以更好地理解和分类各种负荷特性;其次,分解用户侧用电负荷时间序列,利用时间序列分析方法处理和建模这些数据;最后,比较和融合时间序列各模型的输出结果,以获得更准确的预测结果.结果表明,该预测方法能充分捕捉并理解用户用电行为,预测精度高,具有一定的应用价值.
Abstract
In the new power system,user electricity consumption behavior is complex and dynamic,and most of it comes from multiple sources of data.The diverse data sequences lead to low accuracy in electricity load prediction,which affects the prediction results.Therefore,a new research on precise prediction of user side electricity load in power systems is proposed.Firstly,collect a large amount of user side electricity load data and cluster these data to better understand and classify various load characteristics.Secondly,decompose the user side electricity load time series,and use time series analysis methods to process and model these data.Finally,compare and integrate the output results of various models in the time series to obtain more accurate prediction results.The results indicate that the prediction method can fully capture and understand user electricity consumption behavior,with high prediction accuracy and certain application value.
NETL
NSTL
万方数据