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Short-Term Load Forecasting using a Cluster of Neural Networks for the Greek Energy Market

Authors:

Apostolos N. Adamakos,

Michalis K. TitsiasAuthors Info & Claims

SETN '16: Proceedings of the 9th Hellenic Conference on Artificial Intelligence

Article No.: 15, Pages 1 - 6

https://doi.org/10.1145/2903220.2903222

Published: 18 May 2016 Publication History

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Abstract

In the context of the liberalization of the Greek Energy Market, load forecasting is essential in various system programming procedures. Short-term load forecasting extends from one to seven days, although in this paper a model is proposed for the next calendar day in step of sixty minutes. The objective is to design and implement a software-based short-term load forecasting model for the Greek interconnected transmission system that will show improved performance compared to previous methods. The proposed model introduces a categorization of the forecasted days along with a dedicated artificial neural network for each category. Appropriate input vectors are selected at the training process for each custom-built network.

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Stentoumi ONousi PTzelepi MTefas A(2023)AnIO: anchored input–output learning for time-series forecastingNeural Computing and Applications10.1007/s00521-023-09175-836:6(2683-2693)Online publication date: 22-Nov-2023
https://doi.org/10.1007/s00521-023-09175-8
Emmanouilidis GTzelepi MTefas A(2022)Short-Term Electric Load Demand Forecasting on Greek Energy Market using Deep Learning: A comparative study2022 Panhellenic Conference on Electronics & Telecommunications (PACET)10.1109/PACET56979.2022.9976351(1-4)Online publication date: 2-Dec-2022
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Maragkos NTzelepi MPassalis NAdamakos ATefas A(2022)Electric load demand forecasting on Greek Energy Market using lightweight neural networks2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)10.1109/IVMSP54334.2022.9816189(1-5)Online publication date: 26-Jun-2022
https://doi.org/10.1109/IVMSP54334.2022.9816189
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Short-Term Load Forecasting using a Cluster of Neural Networks for the Greek Energy Market
1. Computing methodologies
  1. Machine learning
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Published In

SETN '16: Proceedings of the 9th Hellenic Conference on Artificial Intelligence

May 2016

249 pages

ISBN:9781450337342

DOI:10.1145/2903220

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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EETN: Hellenic Artificial Intelligence Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2016

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SETN '16

SETN '16: 9th Hellenic Conference on Artificial Intelligence

May 18 - 20, 2016

Thessaloniki, Greece

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Stentoumi ONousi PTzelepi MTefas A(2023)AnIO: anchored input–output learning for time-series forecastingNeural Computing and Applications10.1007/s00521-023-09175-836:6(2683-2693)Online publication date: 22-Nov-2023
https://doi.org/10.1007/s00521-023-09175-8
Emmanouilidis GTzelepi MTefas A(2022)Short-Term Electric Load Demand Forecasting on Greek Energy Market using Deep Learning: A comparative study2022 Panhellenic Conference on Electronics & Telecommunications (PACET)10.1109/PACET56979.2022.9976351(1-4)Online publication date: 2-Dec-2022
https://doi.org/10.1109/PACET56979.2022.9976351
Maragkos NTzelepi MPassalis NAdamakos ATefas A(2022)Electric load demand forecasting on Greek Energy Market using lightweight neural networks2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)10.1109/IVMSP54334.2022.9816189(1-5)Online publication date: 26-Jun-2022
https://doi.org/10.1109/IVMSP54334.2022.9816189
Tzelepi MTefas A(2022)Forecasting day-ahead electric load demand on Greek Energy Market2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA56318.2022.9904346(1-4)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IISA56318.2022.9904346
Xu JBaldick R(2019)Day-Ahead Price Forecasting in ERCOT Market Using Neural Network ApproachesProceedings of the Tenth ACM International Conference on Future Energy Systems10.1145/3307772.3331024(486-491)Online publication date: 15-Jun-2019
https://dl.acm.org/doi/10.1145/3307772.3331024
Mahmud KSoetanto DTown G(2018)5.6 Energy Management Softwares and ToolsComprehensive Energy Systems10.1016/B978-0-12-809597-3.00518-6(202-257)Online publication date: 2018
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Saber AAlam A(2017)Short term load forecasting using multiple linear regression for big data2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8285261(1-6)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8285261

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