AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings

Dalia Mohammed Talat Ebrahim Ali, Violeta Motuzienė () and Rasa Džiugaitė-Tumėnienė
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Dalia Mohammed Talat Ebrahim Ali: Lithuanian Energy Institute, Breslaujos Str. 3, LT-44403 Kaunas, Lithuania
Violeta Motuzienė: Department of Buildings Energetics, Faculty of Environmental Engineering, Vilnius Gediminas Technical University, Sauletekio Av. 11, LT-10223 Vilnius, Lithuania
Rasa Džiugaitė-Tumėnienė: Department of Buildings Energetics, Faculty of Environmental Engineering, Vilnius Gediminas Technical University, Sauletekio Av. 11, LT-10223 Vilnius, Lithuania

Energies, 2024, vol. 17, issue 17, 1-35

Abstract: Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively).

Keywords: artificial intelligence; buildings; energy efficiency; saving potential; HVAC; BEMS (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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