Using surrogate modelling and stochastic optimization for optimal day-ahead demand response strategies under weather uncertainty
Bibliographic record
Abstract
Demand response (DR) programs are a promising way to increase the stability of the electrical grid and enable greater uptake of intermittent and renewable energy sources by balancing the supply and demand of electricity. For buildings, demand flexibility is defined as the ability to shift their energy consumption away from peak periods (termed a ‘demand event’). Heating, ventilation and air conditioning (HVAC) systems of buildings can provide such flexibility. However, the demand response flexibility of HVAC systems is sensitive to the weather.A surrogate modelling approach is proposed to allow sub-hourly stochastic modelling to be coupled with a robustness analysis within reasonable computational time. The surrogate model is a machine learning model used as a fast approximation of a dynamic energy model; we develop a formulation to obtain time-series outputs from the surrogate model. A method to quantify the error between the optimal solution of the full optimization using the dynamic energy simulation and the surrogate-based approach is developed. The results show that the proposed method reduces computational time by 90% while introducing only a 3% error. This makes the proposed method a potential solution for day-ahead demand-response optimization with consideration of uncertainty.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".