The use of artificial neural networks (ANN) in the prediction of energy consumption of air-source heat pump in retrofit residential housing
Bibliographic record
Abstract
Abstract Machine learning algorithms using Artificial Neural Network (ANN) were developed to predict the performance of heat pump systems in retrofit residential housing. The study attempts to address the research gap in the application of machine learning algorithms to real-life field measurements as a case study. Rowhouse units with electric resistance baseboard heating were retrofitted with Ductless Air Source Heat Pumps (DASHPs). Sensors were installed to collect the energy consumption data during the baseboard and DASHP monitoring periods. Linear and quadratic regression methods following the International Performance Measurement and Verification Protocol (IPMVP) were applied to predict energy consumption based on outdoor temperature and heating degree days. These predictions were compared against results from ANN models based on Levenberg-Marquardt algorithms using the hour of the day, day of the week, outdoor temperature, wind speed and direction, relative humidity, condition and indoor temperature as inputs. Preliminary results indicate that predictions from ANN models produced higher correlation of determination than those from IPMVP regression analysis.
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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) | 0.000 | 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 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".