Deriving optimal direct load control sequences for HVAC systems of small commercial buildings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Direct load control (DLC) for building HVAC systems, through preconditioning and setup/setback sequences, can substantially reduce electricity consumption during peak periods. Yet, the effectiveness of a DLC sequence strictly depends on the thermophysical attributes of a building and its occupants’ tolerance to variations in the thermal environment. The current one-size-fits-all approach to DLC disregards the inter-building diversity of these factors. This paper demonstrates the inter-building diversity of preconditioning and setup/setback needs by deriving unique DLC sequences for different buildings. To this end, variants of an EnergyPlus model representing a small commercial building in Toronto, Ontario are created by altering its envelope, HVAC capacity, and occupants’ temperature preference characteristics. Through a metaheuristic search, personalized DLC sequences that minimize the HVAC-related electricity costs and the time spent outside a preferred temperature range are estimated for each variant. These personalized DLC sequences were compared with six baseline DLC sequences. Unlike the baseline DLC sequences, the optimal sequences could attain an average of 20% reduction in HVAC-related electricity costs while keeping the time spent outside the preferred temperature ranges under 3% for all variants. Practical application This paper presents an optimization method to derive unique direct load control sequences demonstrating the inter-building diversity of preconditioning and setup/setback needs. The method is tested on a range of buildings with varying characteristics in a simulation environment. The findings of the study are useful in the domains of HVAC controls, demand response, and electrification of HVAC systems.
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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 | 0.001 | 0.001 |
| 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 it