Development of Evaluation Model for Building Energy Usage: Methodology Development and Case Study on Day-Care Centers in South Korea

Jinhyung Park, Kwangwon Choi, Chan-Hyuk Mo, Abu Talib, Semi Park, Deuk-Woo Kim and Jaewan Joe ()
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Jinhyung Park: Building Performance Analysis Group, EG Solutions, 220 Gonghang-daero, Gangseo-gu, Seoul 07806, Republic of Korea
Kwangwon Choi: Department of Smart City Engineering, INHA University, Inha-ro 100, Michuhol-gu, Incheon 22212, Republic of Korea
Chan-Hyuk Mo: Department of Data Science, INHA University, Inha-ro 100, Michuhol-gu, Incheon 22212, Republic of Korea
Abu Talib: Department of Smart City Engineering, INHA University, Inha-ro 100, Michuhol-gu, Incheon 22212, Republic of Korea
Semi Park: Institute of Industrial Science and Technology, INHA University, Inha-ro 100, Michuhol-gu, Incheon 22212, Republic of Korea
Deuk-Woo Kim: Department of Living and Built Environment Research, Korea Institute of Civil Engineering and Building Technology, 283, Goyang-daero, Ilsanseo-gu, Goyang 10223, Republic of Korea
Jaewan Joe: Department of Smart City Engineering, INHA University, Inha-ro 100, Michuhol-gu, Incheon 22212, Republic of Korea

Sustainability, 2025, vol. 17, issue 18, 1-28

Abstract: This study proposes a methodology for fairly assessing the building energy usage level of occupants using a public open dataset. A case study of day-care centers in South Korea was conducted to demonstrate the methodology. An open dataset of monthly building energy consumption in the day-care centers was obtained and grouped based on thermal performance (e.g., U-value). For each performance group, monthly electricity consumption (representing cooling demand), gas consumption (representing heating demand), and energy consumption were segmented using k-means clustering into heavy, medium, and light users. For each user cluster, representative monthly trajectories were ascertained by averaging the values. Using the input variables of the building performance and environmental factors, the machine learning-based evaluation models were developed to purely infer the impact of the occupants on energy consumption (monthly trajectories). All models exhibited reasonable performance (12% cv(RMSE) in the worst case); the linear regression model is recommended for its simplicity and applicability in policymaking and decision-making contexts. Finally, the efficacy of the developed model in evaluating energy usage levels is presented with an example.

Keywords: building energy open dataset; machine learning; energy usage evaluation; user behavior (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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