Transfer Learning for the Prediction of Energy Performance of Water-Cooled Electric Chillers: Grey-Box Models Versus Deep Neural Network (DNN) Models
Bibliographic record
Abstract
The development of data-driven prediction models of energy performance of HVAC equipment, such as chillers, depends on the quality and quantity of measurement data for the model training. The practical applications always struggle with the credibility of results when the training dataset of an existing chiller is relatively small. Moreover, when the energy analyst needs to develop a reliable predictive model of a new chiller, the manufacturer’s proprietary data are not always available. The transfer learning method can soften these constraints and can help in the development of a predictive model that captures the knowledge from the available chiller, called the source chiller, using a small dataset, and apply it to a new chiller, called the target chiller. The paper presents the successful application of transfer learning strategies by using grey-box models and DNN models for the prediction of chillers performance, when measurement data are recorded at 15 min time intervals by the building automation system (BAS) and used for training and testing. The paper confirms the initial hypothesis that both the grey-box models and DNN models of the source chiller from July 2013 predict well the energy performance of the target chiller with measurement datasets from 2016. The DNN models perform slightly better than the grey-box models. The pre-trained grey-box models and DNN models, respectively, are transferred to the target chiller using three strategies: SelfL, TLS0, and TLS1, and the results are compared. SelfL strategy trains and tests the models only with the target data. TLS0 strategy directly transfers the models from the source chiller to the target chiller. TLS1 strategy transfers the models, pre-trained with an extended dataset that is composed of training dataset of Ds and training dataset of Dt. Finally, the models are tested with another set of testing data. The difference in computation times of these two types of models is not significant for preventing the use of DNN models for the applications within the BAS, when compared with grey-box 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".