Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review

Medehou Elogni Segbotangni; Guy Clarence Semassou; Armand Fopah-Lele; Kouamy Victorin Chegnimonhan; Emmanuel Tanyi

doi:doi:10.11648/j.sjee.20251304.11

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| Peer-Reviewed

Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review

Medehou Elogni Segbotangni^*

, Guy Clarence Semassou

, Armand Fopah-Lele

, Kouamy Victorin Chegnimonhan

, Emmanuel Tanyi

Published in Science Journal of Energy Engineering (Volume 13, Issue 4)

Received: 3 November 2025 Accepted: 17 November 2025 Published: 17 December 2025

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Abstract

Improving the energy efficiency of buildings is a major challenge for sustainable development, particularly in countries such as Benin where energy resources are limited while demand continues to grow. The objective of this article is to analyse and synthesize existing approaches for predicting building energy consumption, and to propose a theoretical framework for developing an artificial intelligence–based predictive model adapted to the local context. To achieve this objective, a systematic review of the literature was conducted, focusing on three categories of energy prediction methods: technical approaches, artificial intelligence (AI) approaches, and hybrid approaches. The methodological analysis highlights that while technical and hybrid models rely on thermodynamic equations, AI-based models use historical data to predict future energy consumption based on multiple environmental and operational parameters. The results of the review show that AI-based methods-particularly multiple linear regression, artificial neural networks, and ensemble techniques such as random forests-offer high predictive accuracy and greater adaptability, making them increasingly popular for building energy management. By examining environmental, socio-economic, and technological factors influencing energy use, the study proposes a structured theoretical framework integrating machine learning algorithms to improve prediction performance. The review also identifies research gaps and outlines a methodological pathway for developing an AI-driven predictive model tailored to Benin’s climatic and infrastructural context.

Published in	Science Journal of Energy Engineering (Volume 13, Issue 4)
DOI	10.11648/j.sjee.20251304.11
Page(s)	167-191
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Energy Consumption Prediction, Artificial Intelligence, Linear Regression, Artificial Neural Networks, Random Forest, Benin

References

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Segbotangni, M. E., Semassou, G. C., Fopah-Lele, A., Chegnimonhan, K. V., Tanyi, E. (2025). Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review. Science Journal of Energy Engineering, 13(4), 167-191. https://doi.org/10.11648/j.sjee.20251304.11

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Segbotangni, M. E.; Semassou, G. C.; Fopah-Lele, A.; Chegnimonhan, K. V.; Tanyi, E. Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review. Sci. J. Energy Eng. 2025, 13(4), 167-191. doi: 10.11648/j.sjee.20251304.11

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Segbotangni ME, Semassou GC, Fopah-Lele A, Chegnimonhan KV, Tanyi E. Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review. Sci J Energy Eng. 2025;13(4):167-191. doi: 10.11648/j.sjee.20251304.11

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@article{10.11648/j.sjee.20251304.11,
  author = {Medehou Elogni Segbotangni and Guy Clarence Semassou and Armand Fopah-Lele and Kouamy Victorin Chegnimonhan and Emmanuel Tanyi},
  title = {Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review},
  journal = {Science Journal of Energy Engineering},
  volume = {13},
  number = {4},
  pages = {167-191},
  doi = {10.11648/j.sjee.20251304.11},
  url = {https://doi.org/10.11648/j.sjee.20251304.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjee.20251304.11},
  abstract = {Improving the energy efficiency of buildings is a major challenge for sustainable development, particularly in countries such as Benin where energy resources are limited while demand continues to grow. The objective of this article is to analyse and synthesize existing approaches for predicting building energy consumption, and to propose a theoretical framework for developing an artificial intelligence–based predictive model adapted to the local context. To achieve this objective, a systematic review of the literature was conducted, focusing on three categories of energy prediction methods: technical approaches, artificial intelligence (AI) approaches, and hybrid approaches. The methodological analysis highlights that while technical and hybrid models rely on thermodynamic equations, AI-based models use historical data to predict future energy consumption based on multiple environmental and operational parameters. The results of the review show that AI-based methods-particularly multiple linear regression, artificial neural networks, and ensemble techniques such as random forests-offer high predictive accuracy and greater adaptability, making them increasingly popular for building energy management. By examining environmental, socio-economic, and technological factors influencing energy use, the study proposes a structured theoretical framework integrating machine learning algorithms to improve prediction performance. The review also identifies research gaps and outlines a methodological pathway for developing an AI-driven predictive model tailored to Benin’s climatic and infrastructural context.},
 year = {2025}
}

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T1  - Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review
AU  - Medehou Elogni Segbotangni
AU  - Guy Clarence Semassou
AU  - Armand Fopah-Lele
AU  - Kouamy Victorin Chegnimonhan
AU  - Emmanuel Tanyi
Y1  - 2025/12/17
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N1  - https://doi.org/10.11648/j.sjee.20251304.11
DO  - 10.11648/j.sjee.20251304.11
T2  - Science Journal of Energy Engineering
JF  - Science Journal of Energy Engineering
JO  - Science Journal of Energy Engineering
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EP  - 191
PB  - Science Publishing Group
SN  - 2376-8126
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AB  - Improving the energy efficiency of buildings is a major challenge for sustainable development, particularly in countries such as Benin where energy resources are limited while demand continues to grow. The objective of this article is to analyse and synthesize existing approaches for predicting building energy consumption, and to propose a theoretical framework for developing an artificial intelligence–based predictive model adapted to the local context. To achieve this objective, a systematic review of the literature was conducted, focusing on three categories of energy prediction methods: technical approaches, artificial intelligence (AI) approaches, and hybrid approaches. The methodological analysis highlights that while technical and hybrid models rely on thermodynamic equations, AI-based models use historical data to predict future energy consumption based on multiple environmental and operational parameters. The results of the review show that AI-based methods-particularly multiple linear regression, artificial neural networks, and ensemble techniques such as random forests-offer high predictive accuracy and greater adaptability, making them increasingly popular for building energy management. By examining environmental, socio-economic, and technological factors influencing energy use, the study proposes a structured theoretical framework integrating machine learning algorithms to improve prediction performance. The review also identifies research gaps and outlines a methodological pathway for developing an AI-driven predictive model tailored to Benin’s climatic and infrastructural context.
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Medehou Elogni Segbotangni

Mechanical and Energy Engineering Department, Polytechnic School of Abomey-Calavi, Abomey-Calavi, Benin

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http://orcid.org/0000-0002-0931-5581
Guy Clarence Semassou

Mechanical and Energy Engineering Department, Polytechnic School of Abomey-Calavi, Abomey-Calavi, Benin

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http://orcid.org/0000-0002-2964-8164
Armand Fopah-Lele

Mechanical and Industrial Engineering Department, University of Buea, Buea, Cameroon

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http://orcid.org/0000-0003-2095-7953
Kouamy Victorin Chegnimonhan

National Center of Scientifics Researches, University of Nantes, Nantes, France;Benin National Center of Research and Innovation, University of Abomey-Calavi, Abomey-Calavi, Benin

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http://orcid.org/0000-0001-5679-9155
Emmanuel Tanyi

Electrical and Electronic Engineering Department, University of Buea, Buea, Cameroon

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Segbotangni, M. E., Semassou, G. C., Fopah-Lele, A., Chegnimonhan, K. V., Tanyi, E. (2025). Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review. Science Journal of Energy Engineering, 13(4), 167-191. https://doi.org/10.11648/j.sjee.20251304.11

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Segbotangni, M. E.; Semassou, G. C.; Fopah-Lele, A.; Chegnimonhan, K. V.; Tanyi, E. Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review. Sci. J. Energy Eng. 2025, 13(4), 167-191. doi: 10.11648/j.sjee.20251304.11

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AMA Style

Segbotangni ME, Semassou GC, Fopah-Lele A, Chegnimonhan KV, Tanyi E. Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review. Sci J Energy Eng. 2025;13(4):167-191. doi: 10.11648/j.sjee.20251304.11

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@article{10.11648/j.sjee.20251304.11,
  author = {Medehou Elogni Segbotangni and Guy Clarence Semassou and Armand Fopah-Lele and Kouamy Victorin Chegnimonhan and Emmanuel Tanyi},
  title = {Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review},
  journal = {Science Journal of Energy Engineering},
  volume = {13},
  number = {4},
  pages = {167-191},
  doi = {10.11648/j.sjee.20251304.11},
  url = {https://doi.org/10.11648/j.sjee.20251304.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjee.20251304.11},
  abstract = {Improving the energy efficiency of buildings is a major challenge for sustainable development, particularly in countries such as Benin where energy resources are limited while demand continues to grow. The objective of this article is to analyse and synthesize existing approaches for predicting building energy consumption, and to propose a theoretical framework for developing an artificial intelligence–based predictive model adapted to the local context. To achieve this objective, a systematic review of the literature was conducted, focusing on three categories of energy prediction methods: technical approaches, artificial intelligence (AI) approaches, and hybrid approaches. The methodological analysis highlights that while technical and hybrid models rely on thermodynamic equations, AI-based models use historical data to predict future energy consumption based on multiple environmental and operational parameters. The results of the review show that AI-based methods-particularly multiple linear regression, artificial neural networks, and ensemble techniques such as random forests-offer high predictive accuracy and greater adaptability, making them increasingly popular for building energy management. By examining environmental, socio-economic, and technological factors influencing energy use, the study proposes a structured theoretical framework integrating machine learning algorithms to improve prediction performance. The review also identifies research gaps and outlines a methodological pathway for developing an AI-driven predictive model tailored to Benin’s climatic and infrastructural context.},
 year = {2025}
}

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TY  - JOUR
T1  - Optimisation of Building Energy Management in the Tropics Using Artificial Intelligence Algorithms: Scientific Review
AU  - Medehou Elogni Segbotangni
AU  - Guy Clarence Semassou
AU  - Armand Fopah-Lele
AU  - Kouamy Victorin Chegnimonhan
AU  - Emmanuel Tanyi
Y1  - 2025/12/17
PY  - 2025
N1  - https://doi.org/10.11648/j.sjee.20251304.11
DO  - 10.11648/j.sjee.20251304.11
T2  - Science Journal of Energy Engineering
JF  - Science Journal of Energy Engineering
JO  - Science Journal of Energy Engineering
SP  - 167
EP  - 191
PB  - Science Publishing Group
SN  - 2376-8126
UR  - https://doi.org/10.11648/j.sjee.20251304.11
AB  - Improving the energy efficiency of buildings is a major challenge for sustainable development, particularly in countries such as Benin where energy resources are limited while demand continues to grow. The objective of this article is to analyse and synthesize existing approaches for predicting building energy consumption, and to propose a theoretical framework for developing an artificial intelligence–based predictive model adapted to the local context. To achieve this objective, a systematic review of the literature was conducted, focusing on three categories of energy prediction methods: technical approaches, artificial intelligence (AI) approaches, and hybrid approaches. The methodological analysis highlights that while technical and hybrid models rely on thermodynamic equations, AI-based models use historical data to predict future energy consumption based on multiple environmental and operational parameters. The results of the review show that AI-based methods-particularly multiple linear regression, artificial neural networks, and ensemble techniques such as random forests-offer high predictive accuracy and greater adaptability, making them increasingly popular for building energy management. By examining environmental, socio-economic, and technological factors influencing energy use, the study proposes a structured theoretical framework integrating machine learning algorithms to improve prediction performance. The review also identifies research gaps and outlines a methodological pathway for developing an AI-driven predictive model tailored to Benin’s climatic and infrastructural context.
VL  - 13
IS  - 4
ER  -

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