Data-driven prediction models of multi-dimensional energy consumed in public buildings
Bibliographic record
Abstract
Abstract Due to the intense pressure from energy shortage and environmental protection, an accurate prediction of building energy consumption is crucial for different energy conservation applications and policies. Besides simulation models and traditional statistical approaches, a data-driven modelling based on energy records provides new opportunities for predicting the building energy demand. This research is conducted based on the whole procedure of data mining with limited datasets, by making use of machine learning techniques and mathematical statistics. Especially, regarding the temporal and the architectural scales, models can be categorized into the short-term prediction, medium-term prediction and long-term prediction of classified energy consumptions, which also represent different modelling characteristics derived from mass data, limited data and poor data respectively. During the modelling process, the fuzzy C-means clustering and the interdisciplinary Lorenz curve were utilized to recognize different energy patterns. Afterwards, models of the nonlinear Support Vector Regression, the Grey model and the traditional polynomial regression were utilized respectively to output the predicted sequence. In summary, with datasets in current energy platforms, this paper presents a study of data-driven models based on energy records considering the nonlinear and uncertain features of different multi-dimensional models.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".