Data-driven short-term load forecasting for heating and cooling demand in office buildings
Bibliographic record
Abstract
Abstract Short-term forecasts of energy demand in buildings serve as key information for various operational schemes such as predictive control and demand response programs. Despite this, developing forecast models for heating and cooling loads has received little attention in the literature compared to models for electricity load. In this paper, we present data-driven approaches to forecast hourly heating and cooling energy use in office buildings based on temporal, autoregressive, and exogenous variables. The proposed models calculate hourly loads for a horizon between one hour and 12 hours ahead. Individual models based on artificial neural networks (ANN) and change-point models (CPM) as well as a hybrid of the two methods are developed. A case study is conducted based on hourly thermal load data collected from several office buildings located on the same campus in Ottawa, Canada. The models are trained with more than two years of hourly energy-use data and tested on a separate part of the dataset to enable unbiased validation. The results show that the ANN model can achieve higher forecasting accuracy for the longest forecast horizon and outperforms the results obtained by a Naïve approach and the CPM. However, the performance of the hybrid CPM-ANN method is superior compared to individual models for all studied buildings.
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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 | 0.001 |
| 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".