A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics

Jihoon Moon, Kyu Hyung Kim, Yongsung Kim, Eenjun Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Citations (Scopus)

Abstract

One key issue for stable power supply is to forecast electric load accurately. Since buildings of the same type show similar power consumption patterns, it should be considered for accurate electric load forecast. In particular, university buildings show various electric loads depending on time and other external factors. In this paper, we propose a short-term load forecast model for educational buildings using 2-stage predictive analytics for the effective operation of their power system. To do that, we collect the electric load data of five years from a university campus. Next, we consider the electric load pattern by using the moving average method according to the day of the week. Next, we predict the daily electric load using the random forest method and finally evaluate its performance using the time series cross-validation. The experimental results show that our forecasting model outperforms other competing methods in terms of prediction accuracy.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages219-226
Number of pages8
ISBN (Electronic)9781538636497
DOIs
Publication statusPublished - 2018 May 25
Event2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018 - Shanghai, China
Duration: 2018 Jan 152018 Jan 18

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018

Other

Other2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
Country/TerritoryChina
CityShanghai
Period18/1/1518/1/18

Keywords

  • Electric Load Forecasting
  • Forecasting Model
  • Machine Learning
  • Moving Average
  • Random Forest

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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