Data Mining Frameworks for Energy Consumption Reduction of Existing Buildings
Bibliographic record
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. \nThe 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".