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Analyzing Patterns and Predictive Models of Energy and Water Consumption in Schools

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  • Hana Begić Juričić

    (Faculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Vladimir Prelog Street 3, 31000 Osijek, Croatia)

  • Hrvoje Krstić

    (Faculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Vladimir Prelog Street 3, 31000 Osijek, Croatia)

Abstract

Schools are major consumers of energy and water, significantly influencing environmental sustainability and operational budgets. This study presents a comprehensive review of global trends in energy and water consumption in school buildings, identifying key factors that shape usage patterns, such as the geographic location, climate, building characteristics, and occupancy levels. A particular focus is placed on the role of predictive models in enhancing resource efficiency. The review found that energy consumption in schools varies widely, with heating, lighting, and cooling systems being the primary contributors. In contrast, research on water consumption—especially predictive modeling—is notably scarce, with no studies found that focused specifically on school buildings. This highlights a critical gap in the literature. This study evaluated the existing predictive approaches, including regression analyses, machine learning algorithms, and statistical models, which offer valuable tools for forecasting consumption and guiding targeted efficiency interventions. The findings underscore the urgent need for data-driven strategies to support sustainable resource management in educational facilities.

Suggested Citation

  • Hana Begić Juričić & Hrvoje Krstić, 2025. "Analyzing Patterns and Predictive Models of Energy and Water Consumption in Schools," Sustainability, MDPI, vol. 17(12), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5514-:d:1679558
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    References listed on IDEAS

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    2. Cao, Wenqiang & Yu, Junqi & Chao, Mengyao & Wang, Jingqi & Yang, Siyuan & Zhou, Meng & Wang, Meng, 2023. "Short-term energy consumption prediction method for educational buildings based on model integration," Energy, Elsevier, vol. 283(C).
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