Using surrogate modelling and stochastic optimization for optimal day-ahead demand response strategies under weather uncertainty
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
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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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 | 0.001 |
| 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