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Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features

Global Energy Interconnection 2023年第3期6卷 285-296页

作者： Fan Sun Yaojia Huo Lei Fu Huilan Liu Xi Wang Yiming Ma State Grid Ningxia Electric Power Technical Research Institute Yinchuan 750002P.R.China College of Electronic Information Engineering Hebei UniversityBaoding 071002P.R.China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBaoding 071003P.R.China

To fully exploit the rich characteristic variation laws of an integrated energy system(IES)and further improve the short-term load-forecasting accuracy,a load-forecasting method is proposed for an IES based on LSTM and dynamic similar days with *** expansion was performed to construct a comprehensive load day covering the load and meteorological information with coarse and fine time granularity,far and near time *** Gaussian mixture model(GMM)was used to divide the scene of the comprehensive load day,and gray correlation analysis was used to match the scene with the coarse time granularity characteristics of the day to be *** typical days with the highest correlation with the day to be predicted in the scene were selected to construct a“dynamic similar day”by *** key features of adjacent days and dynamic similar days were used to forecast multi-loads with fine time granularity using *** the static features as input and the selection method of similar days based on non-extended single features,the effectiveness of the proposed prediction method was verified.

关键词： Integrated energy system Load forecast Long short-term memory dynamic similar days Gaussian mixture model

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