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Household electricity demand forecasting: benchmarking state-of-the-art methods

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Hans-Arno JacobsenAuthors Info & Claims

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

Pages 233 - 234

https://doi.org/10.1145/2602044.2602082

Published: 11 June 2014 Publication History

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Abstract

We benchmark state-of-the-art methods for forecasting electricity demand on the household level. Our evaluation is based on two data sets containing the power usage on the individual appliance level. Our results indicate that without further refinement the considered advanced state-of-the-art forecasting methods rarely beat corresponding persistence forecasts. Therefore, we also provide an exploration of promising directions for future research.

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A. Veit, C. Goebel, R. Tidke, C. Doblander, and H.-A. Jacobsen. Household electricity demand forecasting - benchmarking state-of-the-art methods. Technical Report arXiv:1404.0200, arXiv.org, 2014.

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H. Ziekow, C. Doblander, C. Goebel, and H.-A. Jacobsen. Forecasting household electricity demand with complex event processing: insights from a prototypical solution. In Proceedings of the Industrial Track of the 13th ACM/IFIP/USENIX International Middleware Conference. ACM, 2013.

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Prodjinotho UChetangny PAgbomahena MZogbochi VMedewou LBarbier GChamagne D(2024)Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032International Journal of Energy and Power Engineering10.11648/j.ijepe.20241302.1113:2(21-31)Online publication date: 2-Apr-2024
https://doi.org/10.11648/j.ijepe.20241302.11
Mansoor HGull MRauf HShaikh IKhalid MArshad N(2024)Graph Convolutional Networks based short-term load forecasting: Leveraging spatial information for improved accuracyElectric Power Systems Research10.1016/j.epsr.2024.110263230(110263)Online publication date: May-2024
https://doi.org/10.1016/j.epsr.2024.110263
Yadav MJamil MRizwan MKapoor R(2023)Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load ForecastingJournal of Electrical and Computer Engineering10.1155/2023/86697962023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/8669796
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Index Terms

Household electricity demand forecasting: benchmarking state-of-the-art methods
1. Computing methodologies
  1. Machine learning

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Information & Contributors

Information

Published In

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

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 June 2014

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Conference

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

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Prodjinotho UChetangny PAgbomahena MZogbochi VMedewou LBarbier GChamagne D(2024)Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032International Journal of Energy and Power Engineering10.11648/j.ijepe.20241302.1113:2(21-31)Online publication date: 2-Apr-2024
https://doi.org/10.11648/j.ijepe.20241302.11
Mansoor HGull MRauf HShaikh IKhalid MArshad N(2024)Graph Convolutional Networks based short-term load forecasting: Leveraging spatial information for improved accuracyElectric Power Systems Research10.1016/j.epsr.2024.110263230(110263)Online publication date: May-2024
https://doi.org/10.1016/j.epsr.2024.110263
Yadav MJamil MRizwan MKapoor R(2023)Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load ForecastingJournal of Electrical and Computer Engineering10.1155/2023/86697962023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/8669796
Mansoor HAli SKhan IArshad NKhan MFaizullah S(2023)Short-Term Load Forecasting Using AMI DataIEEE Internet of Things Journal10.1109/JIOT.2023.329561710:24(22040-22050)Online publication date: 15-Dec-2023
https://doi.org/10.1109/JIOT.2023.3295617
Graditi GBuonanno ACaliano MDi Somma MValenti M(2023)Machine Learning Applications for Renewable-Based Energy SystemsAdvances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy10.1007/978-3-031-26496-2_9(177-198)Online publication date: 15-Jun-2023
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Gupta GMathur JMathur SOrtiz J(2022)A new approach for benchmarking of residential buildingsProceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3563357.3566145(443-449)Online publication date: 9-Nov-2022
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Fujii THayashi VArakaki RRuggiero WBulla RHayashi FKhalil K(2021)A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption PredictionMachines10.3390/machines1001002310:1(23)Online publication date: 28-Dec-2021
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Mansoor HRauf HMubashar MKhalid MArshad N(2021)Past Vector Similarity for Short Term Electrical Load Forecasting at the Individual Household LevelIEEE Access10.1109/ACCESS.2021.30636509(42771-42785)Online publication date: 2021
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Aurangzeb KAlhussein MJavaid KHaider S(2021)A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on ClusteringIEEE Access10.1109/ACCESS.2021.30530699(14992-15003)Online publication date: 2021
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Fujimoto YFujita MHayashi Y(2021)Deep reservoir architecture for short-term residential load forecasting: An online learning scheme for edge computingApplied Energy10.1016/j.apenergy.2021.117176298(117176)Online publication date: Sep-2021
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