research-article

An ensemble model for day-ahead electricity demand time series forecasting

Authors:

Vahan Babushkin,

Wei Lee WoonAuthors Info & Claims

e-Energy '13: Proceedings of the fourth international conference on Future energy systems

Pages 51 - 62

https://doi.org/10.1145/2487166.2487173

Published: 21 May 2013 Publication History

Abstract

In this work, we try to solve the problem of day-ahead prediction of electricity demand using an ensemble forecasting model. Based on the Pattern Sequence Similarity (PSF) algorithm, we implemented five forecasting models using different clustering techniques: K-means model (as in original PSF), Self-Organizing Map model, Hierarchical Clustering model, K-medoids model, and Fuzzy C-means model. By incorporating these five models, we then proposed an ensemble model, named Pattern Forecasting Ensemble Model (PFEM), with iterative prediction procedure. We evaluated its performance on three real-world electricity demand datasets and compared it with those of the five forecasting models individually. Experimental results show that PFEM outperforms all those five individual models in terms of Mean Error Relative and Mean Absolute Error.

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Cited By

Moghimi SGulliver TThirumai Chelvan I(2024)Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A ReviewEnergies10.3390/en1703055517:3(555)Online publication date: 23-Jan-2024
https://doi.org/10.3390/en17030555
Kachalla IGhiaus C(2024)Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future DirectionsEnergies10.3390/en1702044317:2(443)Online publication date: 16-Jan-2024
https://doi.org/10.3390/en17020443
Marvaniya SSingh JGalichet NOtieno Fde Mel GWeldemariam K(2024)Encoding Seasonal Climate Predictions with Modular Neural NetworkICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446114(6105-6109)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10446114
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Index Terms

An ensemble model for day-ahead electricity demand time series forecasting
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
    2. Machine learning approaches

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cover image ACM Conferences

e-Energy '13: Proceedings of the fourth international conference on Future energy systems

January 2013

306 pages

ISBN:9781450320528

DOI:10.1145/2487166

General Chairs:
David Culler
University of California, Berkeley, USA
,
Catherine Rosenberg
University of Waterloo, Canada
,
Program Chairs:
S. Keshav
University of Waterloo, Canada
,
Jim Kurose
University of Massachusetts, USA

Copyright © 2013 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 ACM 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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Published: 21 May 2013

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e-Energy '13

Sponsor:

SIGCOMM

e-Energy '13: The Fourth International Conference on Future Energy Systems

May 21 - 24, 2013

California, Berkeley, USA

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e-Energy '13 Paper Acceptance Rate 40 of 76 submissions, 53%;

Overall Acceptance Rate 160 of 446 submissions, 36%

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

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Cited By

Moghimi SGulliver TThirumai Chelvan I(2024)Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A ReviewEnergies10.3390/en1703055517:3(555)Online publication date: 23-Jan-2024
https://doi.org/10.3390/en17030555
Kachalla IGhiaus C(2024)Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future DirectionsEnergies10.3390/en1702044317:2(443)Online publication date: 16-Jan-2024
https://doi.org/10.3390/en17020443
Marvaniya SSingh JGalichet NOtieno Fde Mel GWeldemariam K(2024)Encoding Seasonal Climate Predictions with Modular Neural NetworkICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446114(6105-6109)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10446114
Pérez-Chacón RAsencio-Cortés GTroncoso AMartínez-Álvarez F(2024)Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumptionFuture Generation Computer Systems10.1016/j.future.2023.12.021154(397-412)Online publication date: May-2024
https://doi.org/10.1016/j.future.2023.12.021
Giampaolo FGatta FPrezioso ECuomo SZhou MFortino GPiccialli F(2023)ENCODE - Ensemble neural combination for optimal dimensionality encoding in time-series forecastingInformation Fusion10.1016/j.inffus.2023.101918100:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.inffus.2023.101918
Criado-Ramón DRuiz LPegalajar M(2023)CUDA-bigPSFExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120661230:COnline publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120661
Chakraborty IDasgupta ARubio-Herrero JNandanoori SKundu SChandan V(2023)Application of Machine Learning for Energy-Efficient BuildingsHandbook of Smart Energy Systems10.1007/978-3-030-97940-9_102(837-858)Online publication date: 5-Aug-2023
https://doi.org/10.1007/978-3-030-97940-9_102
Chakraborty IDasgupta ARubio-Herrero JNandanoori SKundu SChandan V(2023)Application of Machine Learning for Energy-Efficient BuildingsHandbook of Smart Energy Systems10.1007/978-3-030-72322-4_102-1(1-22)Online publication date: 1-Apr-2023
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Shende MSalih SBokde NScholz MOudah AYaseen Z(2022)Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python PackageApplied Sciences10.3390/app1212619412:12(6194)Online publication date: 17-Jun-2022
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