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International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

Machine Learning Approaches for Detecting Interventions and Conditions to Elevate Power Utilization in Established Facilities

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Dr. Arman V. Solberg 1 , Prof. Elina K. Marovic 1 ,
4 Department of Data Science and Computational Analytics Slovenia National Institute of Technology Novara, Slovenia
4 School of Artificial Intelligence and Data Systems University of Slovenia Lunaris, Slovenia

Abstract

Improving energy performance in existing buildings represents a critical challenge in the global transition toward sustainability and climate neutrality. Traditional retrofit strategies often rely on static models and expert-driven decision-making, which may fail to capture the dynamic and complex interactions between building systems, environmental conditions, and occupant behavior. This study investigates the application of machine learning approaches for detecting optimal interventions and operational conditions aimed at enhancing energy utilization in established facilities.

Adopting a research-oriented analytical framework, this study integrates qualitative synthesis of existing optimization models with a conceptual exploration of machine learning techniques, including artificial neural networks, hybrid intelligent systems, and multi-objective optimization algorithms. Drawing on existing literature in building energy performance, retrofit strategies, and computational modeling, the research develops a structured perspective on how data-driven approaches can improve decision-making processes in energy retrofitting.

The findings indicate that machine learning models offer significant advantages in identifying non-linear relationships, predicting energy consumption patterns, and optimizing retrofit scenarios under multiple constraints. Hybrid approaches that combine evolutionary algorithms with neural networks demonstrate superior performance in balancing energy efficiency, cost, and environmental impact. However, challenges persist in terms of data availability, model interpretability, and integration with existing building management systems.

The study contributes to the field by proposing a conceptual framework that integrates machine learning techniques with multi-criteria decision-making models for building retrofits. It highlights the importance of adaptive learning systems capable of continuous improvement based on real-time data. Furthermore, it identifies key limitations and suggests directions for future research, including the need for standardized datasets and improved model transparency.

Overall, this research underscores the transformative potential of machine learning in enhancing energy efficiency in existing buildings, offering a pathway toward more intelligent and sustainable infrastructure systems.

Keywords

Machine Learning, Energy Efficiency, Building Retrofit, Multi-objective Optimization, Artificial Neural Networks

References

Kaklauskas, E. Zavadskas, S. Raslanas, R. Ginevicius, A. Komka, and P. Malinauskas, “Selection of low-e windows in retrofit of public buildings by applying multiple criteria method COPRAS: A Lithuanian case,” Energy and Buildings, vol. 38, no. 5, pp. 454–462, 2006.
Kaklauskas, E. Zavadskas, and S. Raslanas, “Multivariant design and multiple criterion analysis of building refurbishments,” Energy and Buildings, vol. 37, no. 4, pp. 361–372, 2005.
Kaklauskas, E. Zavadskas, and S. Raslanas, “Multivariant design and multiple criterion analysis of building refurbishments,” Energy and Buildings, vol. 37, no. 4, pp. 361–372, 2005.
Kaklauskas, E. Zavadskas, and S. Raslanas, “Multivariant design and multiple criterion analysis of building refurbishments,” Energy and Buildings, vol. 37, no. 4, pp. 361–372, 2005.
Kaklauskas, E. Zavadskas, and S. Raslanas, “Multivariant design and multiple criterion analysis of building refurbishments,” Energy and Buildings, vol. 37, no. 4, pp. 361–372, 2005.
Kaklauskas, E. Zavadskas, and S. Raslanas, “Multivariant design and multiple criterion analysis of building refurbishments,” Energy and Buildings, vol. 37, no. 4, pp. 361–372, 2005.
Advanced Energy Retrofit Guide—Practical Ways to Improve Energy Performance, NREL, Building Technologies Office, US Department of Energy, 2013.
Asdrubali et al., “Life cycle analysis in the construction sector: guiding the optimization of conventional Italian buildings,” Energy and Buildings, 2013.
Burman et al., “Ecosystem-atmosphere carbon and water exchanges of subtropical evergreen and deciduous forests in India,” Energy and Buildings, 2021.
Cabeza et al., “Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: A review,” 2014.
Diakaki, E. Grigoroudis, and D. Kolokotsa, “Towards a multi-objective optimization approach for improving energy efficiency in buildings,” Energy and Buildings, vol. 40, no. 9, pp. 1747–1754, 2008.
Diakaki, E. Grigoroudis, N. Kabelis, D. Kolokotsa, K. Kalaitzakis, and G. Stavrakakis, “A multi-objective decision model for the improvement of energy efficiency in buildings,” Energy, vol. 35, no. 12, pp. 5483–5496, 2010.
Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the Energy Performance of Buildings, Official Journal of the European Communities, 2002 (Brussels, Belgium).
Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings (recast), Official Journal of the European Communities, 2010.
Abel and A. Elmorth, “Buildings and energy—A systematic approach,” Forskningsrådet Formas, 2007.
Asadi, M. Gameiro da Silva, C. H. Antunes, L. Dias, and L. Glicksman, “Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application,” Energy and Buildings, vol. 81, pp. 444–456, 2014.
Asadi, M. G. da Silva, C. H. Antunes, and L. Dias, “A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB,” Building and Environment, vol. 56, pp. 370–378, 2012.
E. Asadi, M. G. da Silva, C. H. Antunes, and L. Dias, “Multi-objective optimization for building retrofit strategies: A model and an application,” Energy and Buildings, vol. 44, pp. 81–87, 2012.
E. Rey, “Office building retrofitting strategies: Multicriteria approach of an architectural and technical issue,” Energy and Buildings, vol. 36, pp. 367–372, 2004.
E. Rey, “Office building retrofitting strategies: Multicriteria approach of an architectural and technical issue,” Energy and Buildings, vol. 36, pp. 367–372, 2004.
E. Rey, “Office building retrofitting strategies: Multicriteria approach of an architectural and technical issue,” Energy and Buildings, vol. 36, pp. 367–372, 2004.
E. Rey, “Office building retrofitting strategies: Multicriteria approach of an architectural and technical issue,” Energy and Buildings, vol. 36, pp. 367–372, 2004.
Energy Performance of Buildings: Climate Neutrality by 2050, News, European Parliament.
Flourentzou and C. A. Roulet, “Elaboration of retrofit scenarios,” Energy and Buildings, vol. 34, pp. 185–192, 2002.
F. Flourentzou and C. A. Roulet, “Elaboration of retrofit scenarios,” Energy and Buildings, vol. 34, pp. 185–192, 2002.
F. Flourentzou and C. A. Roulet, “Elaboration of retrofit scenarios,” Energy and Buildings, vol. 34, pp. 185–192, 2002.
F. Flourentzou and C. A. Roulet, “Elaboration of retrofit scenarios,” Energy and Buildings, vol. 34, pp. 185–192, 2002.
F. Taher, O. AlFandi, M. Al-fairy, H. Al Hamadi, and S. Alrabaee, “DroidDetectMW: A Hybrid intelligent model for Android Malware detection,” Applied Sciences (Switzerland), vol. 13, no. 13, Article 772, 2023.
J. S. Gero, N. Dcruz, and A. D. Radford, “Energy in context—a multicriteria model for building design,” Building and Environment, vol. 18, no. 3, pp. 99–107, 1983.
J. S. Gero, N. Dcruz, and A. D. Radford, “Energy in context—a multicriteria model for building design,” Building and Environment, vol. 18, no. 3, pp. 99–107, 1983.
J. S. Gero, N. Dcruz, and A. D. Radford, “Energy in context—a multicriteria model for building design,” Building and Environment, vol. 18, no. 3, pp. 99–107, 1983.
J. S. Gero, N. Dcruz, and A. D. Radford, “Energy in context—a multicriteria model for building design,” Building and Environment, vol. 18, no. 3, pp. 99–107, 1983.
M. Jaggs and J. Palmer, “Energy performance indoor environmental quality retrofit—a European diagnosis and decision-making method for building refurbishment,” Energy and Buildings, vol. 31, pp. 97–101, 2000.
M. Jaggs and J. Palmer, “Energy performance indoor environmental quality retrofit—a European diagnosis and decision-making method for building refurbishment,” Energy and Buildings, vol. 31, pp. 97–101, 2000.
M. Jaggs and J. Palmer, “Energy performance indoor environmental quality retrofit—a European diagnosis and decision-making method for building refurbishment,” Energy and Buildings, vol. 31, pp. 97–101, 2000.
M. Jaggs and J. Palmer, “Energy performance indoor environmental quality retrofit—a European diagnosis and decision-making method for building refurbishment,” Energy and Buildings, vol. 31, pp. 97–101, 2000.
Nicolae and George-Vlad, “Life cycle analysis in refurbishment of the buildings as intervention practices in energy saving,” Energy and Buildings, 2015.

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