Predicting Domestic Hot Water Demand Using Machine Learning for Predictive Control Purposes
Bibliographic record
Abstract
An important part of a building energy consumption is related to the domestic hot water consumption of its occupants. Predictive controllers are often considered as having the potential to reduce the energy consumption of hot water systems. In this work, a recurrent neural network is trained from the measured domestic hot water consumption of a 40 unit residential building in Quebec City, Canada, to predict the future consumption. It is found that the water consumption profile of the building changes from day to day throughout the year and has an important noise component. A predicting model is developed in this work and is obtained by pairing a recurrent neural network to predict the filtered domestic hot water demand with a random forest to predict the noise signal. The evaluated performances indices for the prediction of the next demand are satisfying (i.e., RMSE of 142.02 L/h and R2 of 0.71). In addition, it is found that the predictions made over the following hour using the same predicting model are accurate and could likely be used in a predictive control context.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".