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E-Adivino: A Novel Framework for Electricity Consumption Prediction Based on Historical Trends

Published: 14 July 2015 Publication History

Abstract

Electricity demand prediction is important for several real world applications such as Demand Response (DR) program for peak demand management. For utilities with many customers, learning a best fit baseline for every consumer may be time consuming. We propose E-Adivino: an electricity forecasting framework that first clusters customers based on their consumption pattern followed by forecasting for each cluster using a generalized baseline projection approach. E-Adivino allows for selection of appropriate models for different consumers based on their demand patterns rather than using a uniform model for all the consumers, as is the practice today. E-Adivino is evaluated for its real world applicability using data from a university campus in India spanning over a year.

References

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Clifford Grimm and DTE Energy. Evaluating baselines for demand response programs. In 2008 AEIC Load Research Workshop, 2008.
[2]
Peter Cappers, Charles Goldman, and David Kathan. Demand response in us electricity markets: Empirical evidence. Energy, 35(4):1526--1535, 2010.
[3]
Miriam L. Goldberg and G. Kennedy Agnew - DNV KEMA Energy. Measurement and verification for demand response. DNV KEMA Energy and Sustainability, Tech. Rep, 2013.
[4]
Volkan Ş Ediger and Sertac Akar. Arima forecasting of primary energy demand by fuel in turkey. Energy Policy, 35(3):1701--1708, 2007.
[5]
Rob J. Hyndman and Anne B. Koehler. Another look at measures of forecast accuracy. International journal of forecasting, 22(4):679--688, 2006.

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  • (2023)Predictive Analysis of Energy Consumption and Electricity Demand Using Machine Learning Techniques2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES)10.1109/ICSSES58299.2023.10200636(1-6)Online publication date: 7-Jul-2023
  • (2019)Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising AutoencodersEnergies10.3390/en1216309112:16(3091)Online publication date: 11-Aug-2019

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  1. E-Adivino: A Novel Framework for Electricity Consumption Prediction Based on Historical Trends

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    cover image ACM Conferences
    e-Energy '15: Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems
    July 2015
    334 pages
    ISBN:9781450336093
    DOI:10.1145/2768510
    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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    Published: 14 July 2015

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    e-Energy '15 Paper Acceptance Rate 20 of 85 submissions, 24%;
    Overall Acceptance Rate 160 of 446 submissions, 36%

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    • (2023)Predictive Analysis of Energy Consumption and Electricity Demand Using Machine Learning Techniques2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES)10.1109/ICSSES58299.2023.10200636(1-6)Online publication date: 7-Jul-2023
    • (2019)Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising AutoencodersEnergies10.3390/en1216309112:16(3091)Online publication date: 11-Aug-2019

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