Analysis of energy performance of university campus buildings using statistical and energy modeling approaches

Fathi, Soheil; Srinivasan, Ravi

doi:10.1109/wsc.2015.7408497

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…In our initial attempt to address this issue, we evaluated the energy performance of two buildings representing a section of UF campus buildings. A set of energy efficiency measures were identified and implemented, assessing their effects on building energy performance [29]. Comparing various time scales, we noticed that hourly prediction resolution was more frequently used in individual level BEPF, while annual prediction resolution was more frequently used in urban level BEPF.…”

Section: Machine Learning Applications In Bepfmentioning

confidence: 99%

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

et al. 2020

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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).

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Section: Machine Learning Applications In Bepfmentioning

confidence: 99%

“…ID 0064) has 21% office areas and 22% classrooms. The space classification percentages were based on the Gross Square Feet (GSF) area of the buildings [29]. Table 1 represents the list of space functionality percentages used in this study.…”

Section: Layer 1: Data Collectionmentioning

confidence: 99%

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

et al. 2020

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“…Ref. [ [6] , [7] , [8] , [9] ]). Educational buildings like schools and universities often present higher occupation levels than other public buildings, and the length of the time spent in the classroom is also higher.…”

Section: Introductionmentioning

confidence: 99%

Measurement of CO2 concentration for occupancy estimation in educational buildings with energy efficiency purposes

Franco

Leccese

2020

Journal of Building Engineering

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“…Such data can be used for efficient decision making at various levels. There is a constant need for enhancing infrastructure performance through leveraging the digital footprint and using data-driven decision tools [1][2][3][4][5]. The idea of smart cities addresses how the advancement and unavoidable use of Information and Communication Technology (ICT) can impact urban development in regards to environmental, financial, and personal satisfaction aspects [6].…”

Section: Introductionmentioning

confidence: 99%

Vector Maps Mobile Application for Sustainable Eco-Driving Transportation Route Selection

2020

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The decisions managing all modes of transportation are currently based on the traffic rate and travel time. However, other factors such as Green House Gas (GHG) emissions, the sustainability index, fuel consumption, and travel costs are not considered. Therefore, more comprehensive methods need to be implemented to improve transportation systems and support users’ decision making in their daily commute. This paper addresses current challenges by utilizing data analytics derived from our proposed mobile application. The proposed application quantifies various factors of each transportation mode including but not limited to the cost, trip duration, fuel consumption, and Carbon Dioxide (CO2) emissions. All calculated travel costs are based on the real-time gas prices and toll fees. The users are also able to navigate to their destination and update the total travel costs in real-time. The emissions data per trip basis are aggregated to provide analytics of emissions usage. The traffic data is collected for the Southern California region and the effectiveness of the application is evaluated by twenty participants from California State University, Long Beach. The results demonstrate the application’s impacts on users’ decision-making and the propriety of the factors used in route selection. The proposed application can foster urban planning and operations vis-à-vis daily commutes, and as a result improve the citizens’ quality of life in various aspects.

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Analysis of energy performance of university campus buildings using statistical and energy modeling approaches

Cited by 10 publications

References 3 publications

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

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

Measurement of CO2 concentration for occupancy estimation in educational buildings with energy efficiency purposes

Vector Maps Mobile Application for Sustainable Eco-Driving Transportation Route Selection

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