AI-Based Campus Energy Use Prediction for Assessing the Effects of Climate Change

Soheil Fathi, Ravi S. Srinivasan, Charles J. Kibert, Ruth L. Steiner and Emre Demirezen
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Soheil Fathi: Urban Building Energy Sensing, Controls, Big Data Analysis, And Visualization (UrbSys) Lab, University of Florida, Gainesville, FL 32611, USA
Ravi S. Srinivasan: Urban Building Energy Sensing, Controls, Big Data Analysis, And Visualization (UrbSys) Lab, University of Florida, Gainesville, FL 32611, USA
Charles J. Kibert: Powell Center for Construction and Environment, University of Florida, Gainesville, FL 32611, USA
Ruth L. Steiner: Center for Health and the Built Environment, University of Florida, Gainesville, FL 32611, USA
Emre Demirezen: Warrington College of Business, University of Florida, Gainesville, FL 32611, USA

Sustainability, 2020, vol. 12, issue 8, 1-22

Abstract: In developed countries, buildings are involved in almost 50% of total energy use and 30% of global annual greenhouse gas emissions. The operational energy needs of buildings are highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based energy modeling of buildings, Artificial Intelligence (AI) has the capability to provide faster and higher accuracy estimates, given buildings’ historic energy consumption data. Looking beyond individual building levels, forecasting building energy performance can help city and community managers have a better understanding of their future energy needs, and to plan for satisfying them more efficiently. Focusing at an urban scale, this research develops a campus energy use prediction tool for predicting the effects of long-term climate change on the energy performance of buildings using AI techniques. The tool comprises four steps: Data Collection, AI Development, Model Validation, and Model Implementation, and can predict the energy use of campus buildings with 90% accuracy. We have relied on energy use data of buildings situated in the University of Florida, Gainesville, Florida (FL). To study the impact of climate change, we have used climate properties of three future weather files of Gainesville, FL, developed by the North American Regional Climate Change Assessment Program (NARCCAP), represented based on their impact: median (year 2063), hottest (2057), and coldest (2041).

Keywords: climate change; building energy performance forecasting; machine learning; urban buildings energy; scenario analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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