Research on uncertainty modeling and decision-making system for intelligent energy management of building complexes based on Bayesian networks
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
The energy consumption problem of building complexes has become increasingly prominent along with the acceleration of urbanization.In order to achieve ef icient energy saving in building complexes, this study proposes a Bayesian network-based uncertainty modeling in decision-making system for energy consumption management.By analyzing the uncertainty factors in the energy consumption data, a Bayesian network model is constructed to predict and analyze the energy consumption.And the uncertainty factors are used as decision variables to construct the energy consumption management decision-making system based on Bayesian network.The experimental results show that the uncertainty model and decision-making system constructed in this paper have more favorable performance compared with other benchmark methods, and exhibit smaller measurement errors in experimental tests.At the same time, the application of this paper's decision-making system for energy consumption management of building complexes can signi icantly reduce management costs, and obtain the double bene its of reducing energy consumption and saving costs.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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