Forecasting energy time-series data using a fuzzy ARTMAP neural network

Willian De Assis Pedrobon Ferreira; Ian Grout; Alexandre Cesar Rodrigues Da Silva

doi:10.1109/ICPEI49860.2020.9431435

Forecasting energy time-series data using a fuzzy ARTMAP neural network

Willian De Assis Pedrobon Ferreira, Ian Grout, Alexandre Cesar Rodrigues Da Silva

Department of Electronics & Computer Engineering

Universidade Estadual Paulista Júlio de Mesquita Filho

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.

Original language	English
Title of host publication	Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781728172408
DOIs	https://doi.org/10.1109/ICPEI49860.2020.9431435
Publication status	Published - 14 Oct 2020
Event	2020 International Conference on Power, Energy and Innovations, ICPEI 2020 - Chiang Mai, Thailand Duration: 14 Oct 2020 → 16 Oct 2020

Publication series

Name	Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020

Conference

Conference	2020 International Conference on Power, Energy and Innovations, ICPEI 2020
Country/Territory	Thailand
City	Chiang Mai
Period	14/10/20 → 16/10/20

Keywords

energy data
fuzzy ARTMAP neural network
time-series forecasting

Access to Document

10.1109/ICPEI49860.2020.9431435

Cite this

De Assis Pedrobon Ferreira, W., Grout, I., & Da Silva, A. C. R. (2020). Forecasting energy time-series data using a fuzzy ARTMAP neural network. In Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020 (pp. 1-4). Article 9431435 (Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPEI49860.2020.9431435

De Assis Pedrobon Ferreira, Willian ; Grout, Ian ; Da Silva, Alexandre Cesar Rodrigues. / Forecasting energy time-series data using a fuzzy ARTMAP neural network. Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1-4 (Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020).

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title = "Forecasting energy time-series data using a fuzzy ARTMAP neural network",

abstract = "Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.",

keywords = "energy data, fuzzy ARTMAP neural network, time-series forecasting",

author = "{De Assis Pedrobon Ferreira}, Willian and Ian Grout and {Da Silva}, {Alexandre Cesar Rodrigues}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Power, Energy and Innovations, ICPEI 2020 ; Conference date: 14-10-2020 Through 16-10-2020",

year = "2020",

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doi = "10.1109/ICPEI49860.2020.9431435",

language = "English",

series = "Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020",

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De Assis Pedrobon Ferreira, W, Grout, I & Da Silva, ACR 2020, Forecasting energy time-series data using a fuzzy ARTMAP neural network. in Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020., 9431435, Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 2020 International Conference on Power, Energy and Innovations, ICPEI 2020, Chiang Mai, Thailand, 14/10/20. https://doi.org/10.1109/ICPEI49860.2020.9431435

Forecasting energy time-series data using a fuzzy ARTMAP neural network. / De Assis Pedrobon Ferreira, Willian; Grout, Ian; Da Silva, Alexandre Cesar Rodrigues.
Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1-4 9431435 (Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.

AB - Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.

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M3 - Conference contribution

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BT - Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020

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De Assis Pedrobon Ferreira W, Grout I, Da Silva ACR. Forecasting energy time-series data using a fuzzy ARTMAP neural network. In Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1-4. 9431435. (Proceedings of the 2020 International Conference on Power, Energy and Innovations, ICPEI 2020). doi: 10.1109/ICPEI49860.2020.9431435

Forecasting energy time-series data using a fuzzy ARTMAP neural network

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