doi: 10.18178/ijml.2025.15.2.1177
Neural Networks to Better Identify Actions and Scenarios for Improving Energy Performance of Existing Buildings
Aboudoul-Manaf Issifou1
,
Smain Femmam2,* ,
Nadia Femmam3 ,
Samia Nefti-Meziani4
- 1Energy Efficient Engineering, CNAM & Engineering School ENSIATE, Paris, France
- 2Networks and Communications, Haute-Alsace University, Mulhouse, France
- 3Mohamed Khider University, Biskra Algeria
- 4Birmingham Institute for Robotics, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom
- Manuscript receivedJanuary 20, 2025
- revisedFebruary 22, 2025
- acceptedMarch 7, 2025
- publishedMay 20, 2025
Abstract
In a sustainable development approach focused on three pillars, reducing the ecological footprint of anthropogenic activities is a major issue. In France, the building sector, which includes the residential and tertiary sectors, is one of the most energy-intensive sectors. Natural gas (fossil energy) is the energy vector most used to meet the significant energy needs of residential buildings. To reduce the carbon footprint, the energy renovation of residential and tertiary buildings can prove to be a key pillar for meeting sustainable development indicators and thus safeguarding our environment. The use of artificial intelligence, in this case neural networks, could make it possible to achieve enormous energy savings since it can analyze the building in its smallest details, studying the performances and specificities of existing energy systems and then propose improvements and renovation scenarios that are much more adapted to the behavior and model of the building. In concrete terms, how can neural networks be used, through learning, to suggest relevant actions for improving energy performance? This article first presents the role that artificial intelligence, in this case neural networks, could play in the context of the energy renovation of existing buildings, then a neural network applied to a project in order to propose actions to improve energy performance and more efficient renovation scenarios. The authors made a combined use of neural networks and genetic algorithms allowing respectively a rapid evaluation and optimization of the data sought.
Keywords
- static analysis
- dynamic analysis
- malware detection
- hybrid approach
- energy performance
How to Cite
Aboudoul-Manaf Issifou, Smain Femmam, Nadia Femmam, and Samia Nefti-Meziani, "Neural Networks to Better Identify Actions and Scenarios for Improving Energy Performance of Existing Buildings," International Journal of Machine Learning, vol. 15, no. 2, pp. 40-44, 2025. https://doi.org/10.18178/ijml.2025.15.2.1177
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).