A Building Energy Consumption Prediction Method Based on Integration of a Deep Neural Network and Transfer Reinforcement Learning
Why this work is in the frame
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Bibliographic record
Abstract
With respect to the problem of the low accuracy of traditional building energy prediction methods, this paper proposes a novel prediction method for building energy consumption, which is based on the seamless integration of the deep neural network and transfer reinforcement learning (DNN-TRL). The method introduces a stack denoising autoencoder to extract the deep features of the building energy consumption, and shares the hidden layer structure to transfer the common information between different building energy consumption problems. The output of the DNN model is used as the input of the Sarsa algorithm to improve the prediction performance of the target building energy consumption. To verify the performance of the DNN-TRL algorithm, based on the data recorded by American Power Balti Gas and Electric Power Company, and compared with Sarsa, ADE-BPNN, and BP-Adaboost algorithms, the experimental results show that the DNN-TRL algorithm can effectively improve the prediction accuracy of the building energy consumption.
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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 it