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The Impact of Climate Change on a University Campus’ Energy Use: Use of Machine Learning and Building Characteristics

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UrbSys (Urban Building Energy, Sensing, Controls, Big Data Analysis, and Visualization) Laboratory, M.E. Rinker, Sr. School of Construction Management, University of Florida, Gainesville, FL 32603, USA
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1 Tigert Hall, University of Florida, Gainesville, FL 32611, USA
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Department of Urban and Regional Planning, University of Florida, 431 Architecture Building, Gainesville, FL 32611, USA
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Department of Statistics, University of Florida, Gainesville, FL 32611, USA
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Author to whom correspondence should be addressed.
Academic Editors: Shi-Jie Cao and Wei Feng
Buildings 2022, 12(2), 108; https://doi.org/10.3390/buildings12020108
Received: 13 December 2021 / Revised: 14 January 2022 / Accepted: 19 January 2022 / Published: 23 January 2022
(This article belongs to the Topic Building Energy and Environment)
Global warming is expected to increase 1.5 °C between 2030 and 2052. This may lead to an increase in building energy consumption. With the changing climate, university campuses need to prepare to mitigate risks with building energy forecasting models. Although many scholars have developed buildings energy models (BEMs), only a few have focused on the interpretation of the meaning of BEM, including climate change and its impacts. Additionally, despite several review papers on BEMs, there is no comprehensive guideline indicating which variables are appropriate to use to explain building energy consumption. This study developed building energy prediction models by using statistical analysis: multivariate regression models, multiple linear regression (MLR) models, and relative importance analysis. The outputs are electricity (ELC) and steam (STM) consumption. The independent variables used as inputs are building characteristics, temporal variables, and meteorological variables. Results showed that categorizing the campus buildings by building type is critical, and the equipment power density is the most important factor for ELC consumption, while the heating degree is the most critical factor for STM consumption. The laboratory building type is the most STM-consumed building type, so it needs to be monitored closely. The prediction models give an insight into which building factors remain essential and applicable to campus building policy and campus action plans. Increasing STM is to raise awareness of the severity of climate change through future weather scenarios. View Full-Text
Keywords: building energy modelling; regression analysis; machine learning; climate change; university campus; energy consumption prediction building energy modelling; regression analysis; machine learning; climate change; university campus; energy consumption prediction
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MDPI and ACS Style

Im, H.; Srinivasan, R.S.; Maxwell, D.; Steiner, R.L.; Karmakar, S. The Impact of Climate Change on a University Campus’ Energy Use: Use of Machine Learning and Building Characteristics. Buildings 2022, 12, 108. https://doi.org/10.3390/buildings12020108

AMA Style

Im H, Srinivasan RS, Maxwell D, Steiner RL, Karmakar S. The Impact of Climate Change on a University Campus’ Energy Use: Use of Machine Learning and Building Characteristics. Buildings. 2022; 12(2):108. https://doi.org/10.3390/buildings12020108

Chicago/Turabian Style

Im, Haekyung, Ravi S. Srinivasan, Daniel Maxwell, Ruth L. Steiner, and Sayar Karmakar. 2022. "The Impact of Climate Change on a University Campus’ Energy Use: Use of Machine Learning and Building Characteristics" Buildings 12, no. 2: 108. https://doi.org/10.3390/buildings12020108

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