A Critical Perspective on Current Research Trends in Building Operation: Pressing Challenges and Promising 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
Despite the development of increasingly efficient technologies and the ever-growing amount of available data from Building Automation Systems (BAS) and connected devices, buildings are still far from reaching their performance potential due to inadequate controls and suboptimal operation sequences. Advanced control methods such as model-based controls or model-based predictive controls (MPC) are widely acknowledged as effective solutions for improving building operation. Although they have been well-investigated in the past, their widespread adoption has yet to be reached. Based on our experience in this field, this paper aims to provide a broader perspective on research trends on advanced controls in the built environment to researchers and practitioners, as well as to newcomers in the field. Pressing challenges are explored, such as inefficient local controls (which must be addressed in priority) and data availability and quality (not as good as expected, despite the advent of the digital era). Other major hurdles that slow down the large-scale adoption of advanced controls include communication issues with BAS and lack of guidelines and standards tailored for controls. To encourage their uptake, cost-effective solutions and successful case studies are required, which need to be further supported by better training and engagement between the industry and research communities. This paper also discusses promising opportunities: while building modelling is already playing a critical role, data-driven methods and data analytics are becoming a popular option to improve buildings controls. High-performance local and supervisory controls have emerged as promising solutions. Energy flexibility appears instrumental in achieving decarbonization targets in the built environment.
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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 | 0.000 |
| 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