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

Fan Sun; Yaojia Huo; Lei Fu; Huilan Liu; Xi Wang; Yiming Ma

doi:10.1016/j.gloei.2023.06.003

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

Smart Grid | 更新时间：2026-10-08

- Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features
- Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features
- 全球能源互联网(英文) 2023年6卷第3期页码：285-296
- 作者机构：
  
  1. State Grid Ningxia Electric Power Technical Research Institute,Yinchuan,PR China,750002
  2. College of Electronic Information Engineering, Hebei University,Baoding,PR China,071002
  3. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University,Baoding,PR China,071003
- 作者简介：
- 基金信息：
- DOI：10.1016/j.gloei.2023.06.003
  中图分类号：
- 网络首发：2023-06-27，
  
  纸质出版：2023-06-27
- 稿件说明：
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Fan Sun, Yaojia Huo, Lei Fu, 等. Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features[J]. 全球能源互联网(英文), 2023,6(3):285-296.

Fan Sun, Yaojia Huo, Lei Fu, et al. Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features[J]. Global Energy Interconnection, 2023, 6(3): 285-296.
Fan Sun, Yaojia Huo, Lei Fu, 等. Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features[J]. 全球能源互联网(英文), 2023,6(3):285-296. DOI： 10.1016/j.gloei.2023.06.003.

Fan Sun, Yaojia Huo, Lei Fu, et al. Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features[J]. Global Energy Interconnection, 2023, 6(3): 285-296. DOI： 10.1016/j.gloei.2023.06.003.

摘要

Abstract

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 multi-features. Feature 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 periods. The 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 forecasted. Five typical days with the highest correlation with the day to be predicted in the scene were selected to construct a “dynamic similar day” by weighting. The key features of adjacent days and dynamic similar days were used to forecast multi-loads with fine time granularity using LSTM. Comparing 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.

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