Integration of artificial intelligence into energy efficiency management of construction facilities: economic and technological analysis
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Abstract
The growth of energy consumption in the construction sector, which accounts for about 40% of global energy consumption, is driving the search for innovative approaches to improving the energy efficiency of buildings. Modern research demonstrates the significant potential of using artificial intelligence (AI) technologies to optimize energy consumption at all stages of the life cycle of construction projects. The present study is aimed at a comprehensive analysis of the effectiveness of integrating machine learning algorithms into building energy management systems and an economic assessment of the implementation of such solutions. The research methodology included a multi-level analysis of energy consumption data from 47 commercial facilities equipped with intelligent control systems, comparative modeling of energy consumption using various machine learning algorithms, and economic analysis using the total cost of ownership methodology. The results demonstrate that the implementation of predictive models based on deep learning algorithms reduces energy consumption by 23.7% (±2.1%) compared to traditional automation systems. The economic effect of the integrated implementation of intelligent systems averages 187.5 rubles/m2 per year with a payback period of 2.3 years for commercial real estate. A correlation (r=0.78) has been established between the accuracy of forecasting energy consumption and the economic efficiency of implementation. The conducted research forms a methodological basis for the integration of AI technologies into energy management of buildings and substantiates the economic feasibility of large-scale implementation of such solutions in the Russian construction sector.
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References
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