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).
Highlights
Campus Energy Use Prediction (CEUP) model can predict the energy use of campus buildings with 90% accuracy;
Reviewing more than 70 journal papers in this field, we found that the majority of Building Energy Performance Forecasting (BEPF) studies focus on combining or comparing various machine learning (ML) methods, as well as categorizing building functionality, characteristics, and consumption patterns
By using additional building data, we can increase the forecasting accuracy levels and develop the CEUP model to be representative of campus energy performance
Summary
The Third United States National Climate Assessment [1] describes climate change consisting of long-term variations in temperature, wind, precipitation, and all other aspects of the Earth climate. There are robust indications that the average temperature of Earth will increase for 2 ◦C in the 21st century [2] This amount of change may sound insignificant, it can cause considerable changes in the Earth’s climate and disturb its natural weather systems. A vast range of national-level building energy demand models were developed in a disaggregated way, varying considerably regarding data input requirements and sociotechnical assumptions about building operation [10]. Their expected results vary considerably based on these assumptions. Construction professionals could benefit from this knowledge in developing techniques and business strategies for sustainable refurbishment
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