Ashouri Sanjani, Milad (2019) Data Mining Frameworks for Energy Consumption Reduction of Existing Buildings. PhD thesis, Concordia University.
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Abstract
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 successfulness of developed methodologies in finding energy saving opportunities through modifying occupant behavior.
Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering |
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Item Type: | Thesis (PhD) |
Authors: | Ashouri Sanjani, Milad |
Institution: | Concordia University |
Degree Name: | Ph. D. |
Program: | Building Engineering |
Date: | September 2019 |
Thesis Supervisor(s): | Haghighat, Fariborz and Fung, Benjamin |
Keywords: | Data Mining Energy smart building |
ID Code: | 986091 |
Deposited By: | Milad Ashouri Sanjani |
Deposited On: | 29 Jun 2021 23:25 |
Last Modified: | 29 Jun 2021 23:25 |
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