Enhancing building energy performance prediction: A fusion of deep learning and first-principles simulation methods
Bibliographic record
Abstract
Accurate energy demand prediction is essential for effective control of energy consumption and generation, enabling optimal energy management and reducing emissions. This study introduces a hybrid model integrating a physics-based EnergyPlus simulation with a long short-term memory (LSTM) neural network to enhance energy demand forecasting for buildings. While physics-based models offer computational efficiency, they often result in significant discrepancies between predicted and actual energy demand. Conversely, LSTM models improve prediction accuracy but struggle with peak forecasting. The proposed hybrid approach leverages the strengths of both methods, combining simulation outputs with LSTM predictions to reduce the normalized mean absolute error (NMAE) and improve peak predictions. A feature importance algorithm is incorporated to identify key variables, such as water heater cycle on count, that influence peak values during the LSTM training process. The hybrid model demonstrates a 5% and 20% reduction in normalized root mean square error (NRMSE) compared to the standalone LSTM and simulation models, respectively, during the warmest week of summer. Slight improvements are also observed during winter, with NRMSE reductions of 3% and 11% for the coldest week. Additionally, the hybrid model outperforms the single LSTM in predicting peak energy demand across various thresholds, as evidenced by superior F 1-scores, precision and recall. While the model is less effective during certain winter periods, its overall robustness in predicting peaks and valleys across seasonal variations underscores its utility for real-world applications.
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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 | 0.000 |
| 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".