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

Jihoon Moon, Kyu Hyung Kim, Yongsung Kim, Een Jun Hwang

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

9 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

Other

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

Fingerprint

Electric load forecasting
Electric loads
Predictive analytics
Time series
Electric power utilization

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

Cite this

Moon, J., Kim, K. H., Kim, Y., & Hwang, E. J. (2018). A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics. In Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018 (pp. 219-226). [8367120] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigComp.2018.00040

A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics. / Moon, Jihoon; Kim, Kyu Hyung; Kim, Yongsung; Hwang, Een Jun.

Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 219-226 8367120.

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

Moon, J, Kim, KH, Kim, Y & Hwang, EJ 2018, A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics. in Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018., 8367120, Institute of Electrical and Electronics Engineers Inc., pp. 219-226, 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018, Shanghai, China, 18/1/15. https://doi.org/10.1109/BigComp.2018.00040
Moon J, Kim KH, Kim Y, Hwang EJ. A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics. In Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 219-226. 8367120 https://doi.org/10.1109/BigComp.2018.00040
Moon, Jihoon ; Kim, Kyu Hyung ; Kim, Yongsung ; Hwang, Een Jun. / A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics. Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 219-226
@inproceedings{f4a4869be65043dcb76cd9b236a82aa4,
title = "A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics",
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.",
keywords = "Electric Load Forecasting, Forecasting Model, Machine Learning, Moving Average, Random Forest",
author = "Jihoon Moon and Kim, {Kyu Hyung} and Yongsung Kim and Hwang, {Een Jun}",
year = "2018",
month = "5",
day = "25",
doi = "10.1109/BigComp.2018.00040",
language = "English",
pages = "219--226",
booktitle = "Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics

AU - Moon, Jihoon

AU - Kim, Kyu Hyung

AU - Kim, Yongsung

AU - Hwang, Een Jun

PY - 2018/5/25

Y1 - 2018/5/25

N2 - 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.

AB - 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.

KW - Electric Load Forecasting

KW - Forecasting Model

KW - Machine Learning

KW - Moving Average

KW - Random Forest

UR - http://www.scopus.com/inward/record.url?scp=85048461574&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048461574&partnerID=8YFLogxK

U2 - 10.1109/BigComp.2018.00040

DO - 10.1109/BigComp.2018.00040

M3 - Conference contribution

AN - SCOPUS:85048461574

SP - 219

EP - 226

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

PB - Institute of Electrical and Electronics Engineers Inc.

ER -