Model predictive control for commercial buildings: trends and opportunities
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
AbstractModel predictive control (MPC) is a recent development that uses modelling and simulation with forecast conditions to exploit building thermal mass in an effort to improve indoor thermal comfort and reduce energy use/cost compared with traditional rule-based control strategies. This paper investigates a range of recent MPC strategies and the direction in which this new technology is trending. The findings indicate that there is little evidence that directly compares the performance of specific optimization algorithms, forecast and simulation parameters (timestep, horizon), and climate forecast accuracy for the same scenario. An analysis of 19 case studies highlights the advantages of MPC compared with conventional control strategies, but also identifies areas that need further improvement. These areas include the optimization strategy, the effects of forecast disturbance assessment, and desirable traits of existing buildings under consideration for MPC implementation. This paper develops a set of target parameters for the types of buildings for which MPC will have the most impact and suggests methods for overcoming shortfalls identified in recently published studies.Keywords: predictive controlscommercial buildingsenergy reductionThis article refers to:Addendum AcknowledgementThe authors gratefully acknowledge the assistance and support provided by Green Power Labs Inc. throughout this research.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe authors appreciate major funding support provided by the Atlantic Canadian Opportunities Agency supporting innovative economic growth in Canada's Atlantic Provinces. Additional funding was provided by MITACS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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