Using Forecasted Daily Maximum Temperatures to Control a Chiller Thermal Storage System
Bibliographic record
Abstract
Residential buildings in Canada and the United States are responsible for approximately 20% of secondary energy consumption. Over the past 25 years, air conditioning has seen the single largest increase of any residential end use. This load currently places a significant peak load on the electrical grid during later afternoon periods during the cooling season. One method to reduce or eliminate this peak load being placed in the grid is the use of a chiller coupled with a thermal storage system. The chiller operates during off-peak periods, predominately over-night to charge the thermal storage tank, and the stored cooling potential is realized to meet the cooling loads during peak periods. In previous studies, the use of a chiller has seen a reduction in annual operating costs, however a significant increase in energy occurs as a result of decreased performance of the chiller. To improve system performance, a new control scheme was developed, which uses the forecasted daily high for the next day to predict the cooling load for the day during peak periods for the day. The predicted cooling load is then used as the set-point for the cold thermal storage tank, allowing the peak cooling load to be met using stored cooling potential. This control scheme was implemented into a modelled house located in each of the 7 major ASHRAE zones, with a storage tank with a previously found optimal tank volume. Across each of the locations, a reduction in annual utility costs and overall energy required to meet the building loads observed, with the total cost savings between 0.3% and 1.5% and total electricity required to meet the cooling demand decreasing by as much as 10.2%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".