This analysis reveals improved energy performance in schools and other buildings using digital twins and machine learning, suggesting greater efficiency and CO₂ reduction.
This paper examines the impact of energy consumption on buildings, focusing on Lithuania. It presents a case study that evaluates the energy performance of various building types – apartment buildings, schools, kindergartens, and a shopping centre – before and after renovation. The research highlights the integration of digital tools, particularly digital twins, in managing and optimising energy use. The study models optimal heating systems using air-to-water, geothermal, and hybrid heat pumps by leveraging historical consumption data and applying machine learning and simulation techniques. The results demonstrate that digitalisation, combined with renewable energy sources, significantly enhances energy efficiency and supports CO₂ neutrality goals. The study concludes that digital twins are practical tools for planning, monitoring, and improving energy performance in urban neighbourhoods, contributing to Lithuania’s broader decarbonisation strategy.
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Bakas et al. (2025) studied this question.
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