Reducing energy waste in post-secondary educational institutions using artificial intelligence
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2012
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Abstract
This thesis focuses on computer-related and lighting energy consumption in post-secondary educational institutions. In this respect, artificial intelligence and data association mining are proposed as tools to identify and reduce energy waste. First, an artificial intelligencebased method for forecasting computer usage is proposed. Based on the models' forecast, workstations can be turned on and off, in order to strike a balance between energy savings and user comfort. The models are evaluated on different datasets and their results compared to commercially available alternatives. Second, a data association mining-based approach is proposed to uncover possible relationships between occupancy patterns and lighting-related energy waste in classrooms. A wireless data collection system is used to log data from both lighting consumption and occupancy states during a year. Next, energy savings results of using the proposed approach are compared to those of an occupancy-activated lighting control system for classrooms.
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Bibliography: p. 136-153
Some pages are in colour.
Some pages are in colour.
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Citation
Motta Cabrera, D. F. (2012). Reducing energy waste in post-secondary educational institutions using artificial intelligence (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/4937