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An in-depth study of forecasting household electricity demand using realistic datasets

Authors:

Ling-Jyh ChenAuthors Info & Claims

e-Energy '14: Proceedings of the 5th international conference on Future energy systems

Pages 145 - 155

https://doi.org/10.1145/2602044.2602055

Published: 11 June 2014 Publication History

Abstract

Data analysis and accurate forecasts of electricity demand are crucial to help both suppliers and consumers understand their detailed electricity footprints and improve their awareness about their impacts to the ecosystem. Several studies of the subject have been conducted in recent years, but they are either comprehension-oriented without practical merits; or they are forecast-oriented and do not consider per-consumer cases. To address this gap, in this paper, we conduct data analysis and evaluate the forecasting of household electricity demand using three realistic datasets of geospatial and lifestyle diversity. We investigate the correlations between household electricity demand and different external factors, and perform cluster analysis on the datasets using an exhaustive set of parameter settings. To evaluate the accuracy of electricity demand forecasts in different datasets, we use the support vector regression method. The results demonstrate that the medium mean absolute percentage error (MAPE) can be reduced to 15.6% for household electricity demand forecasts when proper configurations are used.

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Cited By

Ming YCao J(2018)Electrical Load Prediction in Energy Internet via Linear Correlation Coefficient Approach2018 IEEE International Conference on Energy Internet (ICEI)10.1109/ICEI.2018.00036(157-162)Online publication date: May-2018
https://doi.org/10.1109/ICEI.2018.00036
Ahmed AArab KBouida ZIbnkahla M(2018)Data Communication and Analytics for Smart Grid Systems2018 IEEE International Conference on Communications (ICC)10.1109/ICC.2018.8423021(1-6)Online publication date: May-2018
https://doi.org/10.1109/ICC.2018.8423021
Zhan LChiu DKalyanaraman SSeetharam DShorey RRamchurn SSrivastava M(2015)Encouraging Energy Conservation in Campus Dormitory Via Monitoring and PoliciesProceedings of the 2015 ACM Sixth International Conference on Future Energy Systems10.1145/2768510.2768516(307-312)Online publication date: 14-Jul-2015
https://dl.acm.org/doi/10.1145/2768510.2768516
Show More Cited By

Index Terms

An in-depth study of forecasting household electricity demand using realistic datasets
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

e-Energy '14: Proceedings of the 5th international conference on Future energy systems

June 2014

326 pages

ISBN:9781450328197

DOI:10.1145/2602044

General Chairs:
Jon Crowcroft
University of Cambridge, UK
,
Richard Penty
University of Cambridge, UK
,
Program Chairs:
Jean-Yves Le Boudex
EPFL, Switzerland
,
Prashant Shenoy
University of Massachusetts, Amherst, USA

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 June 2014

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National Science Council Taiwan

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e-Energy '14

Sponsor:

SIGCOMM

e-Energy '14: The Fifth International Conference on Future Energy Systems

June 11 - 13, 2014

Cambridge, United Kingdom

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e-Energy '14 Paper Acceptance Rate 23 of 112 submissions, 21%;

Overall Acceptance Rate 160 of 446 submissions, 36%

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E-Energy '25

Sponsor:
sigenergy

The 16th ACM International Conference on Future and Sustainable Energy Systems

June 17 - 20, 2025

Rotterdam , Netherlands

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Cited By

Ming YCao J(2018)Electrical Load Prediction in Energy Internet via Linear Correlation Coefficient Approach2018 IEEE International Conference on Energy Internet (ICEI)10.1109/ICEI.2018.00036(157-162)Online publication date: May-2018
https://doi.org/10.1109/ICEI.2018.00036
Ahmed AArab KBouida ZIbnkahla M(2018)Data Communication and Analytics for Smart Grid Systems2018 IEEE International Conference on Communications (ICC)10.1109/ICC.2018.8423021(1-6)Online publication date: May-2018
https://doi.org/10.1109/ICC.2018.8423021
Zhan LChiu DKalyanaraman SSeetharam DShorey RRamchurn SSrivastava M(2015)Encouraging Energy Conservation in Campus Dormitory Via Monitoring and PoliciesProceedings of the 2015 ACM Sixth International Conference on Future Energy Systems10.1145/2768510.2768516(307-312)Online publication date: 14-Jul-2015
https://dl.acm.org/doi/10.1145/2768510.2768516
Chelmis CKolte JPrasanna V(2015)Big data analytics for demand responseProceedings of the 2015 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2015.7364011(2223-2232)Online publication date: 29-Oct-2015
https://dl.acm.org/doi/10.1109/BigData.2015.7364011

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