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

Author

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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)

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).

Suggested Citation

  • Soheil Fathi & Ravi S. Srinivasan & Charles J. Kibert & Ruth L. Steiner & Emre Demirezen, 2020. "AI-Based Campus Energy Use Prediction for Assessing the Effects of Climate Change," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3223-:d:346275
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    References listed on IDEAS

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    1. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    2. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2015. "Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, Elsevier, vol. 144(C), pages 261-275.
    3. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    4. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
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    Cited by:

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    2. Prabhsimran Singh & Surleen Kaur & Abdullah M. Baabdullah & Yogesh K. Dwivedi & Sandeep Sharma & Ravinder Singh Sawhney & Ronnie Das, 2023. "Is #SDG13 Trending Online? Insights from Climate Change Discussions on Twitter," Information Systems Frontiers, Springer, vol. 25(1), pages 199-219, February.
    3. Dorota Kamrowska-Załuska, 2021. "Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities," Land, MDPI, vol. 10(11), pages 1-19, November.
    4. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    5. Davidson, Eleni & Schwartz, Yair & Williams, Joe & Mumovic, Dejan, 2024. "Resilience of the higher education sector to future climates: A systematic review of predicted building energy performance and modelling approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).

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