A Recommender System for Predictive Control of Heating Systems in Economic Demand Response Programs
Why this work is in the frame
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Bibliographic record
Abstract
Flexibility from demand-side resources is increasingly required in modern power systems to maintain the dynamic balance between demand and supply. This flexibility comes from elastic users managing controllable loads. In this context, controlling Electric Space Heaters (ESHs) is of particular interest because it can leverage building inner thermal storage capacity to shift consumption while maintaining comfort conditions. Some economic Demand Response (DR) programs have considered exploiting EHSs flexibility potentials in recent years. However, these programs still struggle to engage customers due to the complexity of processing price signals for inexpert users. Therefore, it is necessary to develop automated tools for helping users to operate their loads. Accordingly, this paper presents a recommender system based on Gaussian processes to discover users' valuations of thermal comfort and perform the predictive control of their ESHs. The proposed method enables customers to participate in DR programs and impose their preferences through straightforward queries instead of directly changing control parameters. Validation results demonstrate that users maximize their utility by supplying noiseless and consistent data to the recommender system. Additionally, the suggested approach achieves a higher acceptance rate than other methods from the literature, such as persistency and support vector machines.
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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 | 0.002 | 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 | 0.001 | 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