Evaluation of renewable energy deployment scenarios for building energy management
Bibliographic record
Abstract
According to International Energy Agency (IEA), 35% of total energy is consumed in buildings. Proper management of building energy would effectively improve fossil fuel consumption by integrating Renewable Energy Sources (RES). This paper introduces novel methodology to deploy Renewable Energy Sources (RES) for buildings. The developed methodology composed of two steps: evaluation of RES deployment to a building and evaluation of load-generation scenarios in buildings. At first, the proposed algorithm obtains information about building facilities and structure that can be used to deploy PV, wind turbine and gas generator. Solar and wind profiles are analyzed and integrated with building energy model, which is used to evaluate potential energy generation scenarios. The second step includes the evaluation of different supply—generation scenarios based on load profiles and solar and wind generation profiles. This step will include the minimization of energy loss and will seek effective utilization of generated energy. A case study of domestic home in Toronto, Canada, was chosen as an example to demonstrate the proposed algorithm. Results are shown and analyzed which demonstrate the different scenarios generated for the selected case study based on loads and generation profiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".