Research Area: Data Mining
Many technical solutions have been developed to reduce buildings’ energy consumption, but limited efforts have been made to adequately address the role or action of building occupants in this process. On the other side, Building Management System (BMS) monitors the performance of buildings by recording the data to improve the building operation, control systems and maintenance. Usually, BMS produces a large volume of data throughout the year including information with regard to patterns of energy use, occupant behavior, etc. The availability of this huge data has created an opportunity to extract information to improve the building energy performance through leveraging powerful data analytic tools.
The objectives defined in this thesis lie in developing methodologies to find energy saving opportunities by analyzing data coming from occupants’ energy consumption. Three tasks are defined in this thesis. The first task is to provide a recommender system to alert the occupants to take certain measures in order to reduce their energy consumption through end-use loads. Therefore, the quantification of potential savings is provided upon following recommendations. The proposed methodology is also capable to detect the energy saving measures performed by occupants.
The second task focuses on a systematic comparison procedure between the buildings to make the occupants aware of their rank among other buildings and hence give them clues on how to improve their performance. The third task focuses on developing a framework to create a reference building acting as a reference for a given building. Therefore, the given building can be compared against its reference building. Potential savings are given to the given building along with directions how to achieve them. The results show successful of developed methodologies in finding energy saving opportunities through modifying occupant behavior.
Name of the Researcher: Ashouri Sanjani, Milad
Name of the Supervisor(s): Haghighat, Fariborz and Fung, Benjamin
Year of Completion: 2019
University: Concordia University
Thesis Link: Home Page Url
