Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)

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A Novel Optimized Variant of Machine Learning Algorithm for Accurate Energy Demand Prediction for Tetouan City, Morocco

Authors
Natalie RoSe1, *, Otis Osbourne1, Neil Williams1, Syed Sajjad Hussain Rizvi1, 2
1University of the Commonwealth Caribbean, Kingston, Jamaica
2SZABIST University, Karachi, Pakistan
*Corresponding author. Email: ithod@ucc.edu.jm
Corresponding Author
Natalie RoSe
Available Online 21 December 2023.
DOI
10.2991/978-94-6463-314-6_7How to use a DOI?
Keywords
Machine Learning; Optimization; Hyper-Parameters; Data Driven Energy Management; Robust Energy Management; Power Consumption of Tetouan City Dataset; Bayesian Optimizer
Abstract

Machine learning (ML) algorithms are an essential component of intelligent energy management systems. In the year 2021, a benchmark dataset of the power consumption of Tetouan city was published to train an ML algorithm for accurate energy demand prediction. However, parametric and empirical investigations for the best ML algorithm on this dataset are still undetermined. In this study, an exhaustive parametric evaluation of 26 ML variants is presented to advocate for the best algorithm for energy demand prediction in Tetouan city. After a thorough evaluation, the proposed Bayesian Fine Tree (BFT) outperforms the traditional Fine Tree algorithm. The simulation results provide strong evidence that the BFT is best at predicting the energy demand of Tetouan City.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
21 December 2023
ISBN
10.2991/978-94-6463-314-6_7
ISSN
2589-4900
DOI
10.2991/978-94-6463-314-6_7How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Natalie RoSe
AU  - Otis Osbourne
AU  - Neil Williams
AU  - Syed Sajjad Hussain Rizvi
PY  - 2023
DA  - 2023/12/21
TI  - A Novel Optimized Variant of Machine Learning Algorithm for Accurate Energy Demand Prediction for Tetouan City, Morocco
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 62
EP  - 73
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_7
DO  - 10.2991/978-94-6463-314-6_7
ID  - RoSe2023
ER  -