Electric Power ›› 2021, Vol. 54 ›› Issue (10): 211-216.DOI: 10.11930/j.issn.1004-9649.202103108

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Mid-and-Long Term Load Forecasting Based on Integrated Power Consumption Data

WANG Xingang, ZHU Binruo, GU Zhen   

  1. Electric Power Research Institute of State Grid Shanghai Electric Power Company, Shanghai 200051, China
  • Received:2021-03-17 Revised:2021-09-13 Online:2021-10-05 Published:2021-10-16
  • Supported by:
    This work is supported by Science and Technology Project of SGCC (No.1100-201919158A-0-0-00)

Abstract: Load forecasting is critical for management and security of smart grid system. Traditional methods are usually on the basis of historical power consumption data, and the popularization of multi-meter integration technology makes analysis of integrated energy consumption data more efficient. Towards the issue of load forecasting, with water/power/gas consumption data collected by integrated smart meter as features, two mid-and-long term power consumption forecasting methods are proposed: gaussian process regression (GPR) and relevance vector regression (RVR). Experimental results show the superiority of the proposed method and the significance of integrated energy consumption data for load forecasting problem.

Key words: power consumption forecasting, multi-meter integration, probabilistic model, time series