Addressing Load Forecasting Challenges in Industrial Environments Using Time Series Deep Models
Bibliographic record
Abstract
Energy is one of the most important topics in the modern world, as it affects various aspects of human life and the environment. The current transition era and the growing demand for energy require innovative solutions to optimize energy use and reduce its negative impacts. Effective energy management can lead to improved consumption patterns for consumers and a better understanding and control of the demand for producers. However, energy management is a complex and challenging task that involves multiple factors and uncertainties. In this paper, we focus on the problem of electricity consumption prediction and we discuss several deep learning models that involve long short-term memory networks (LSTM), temporal convolutional networks (TCN) and attention mechanisms. We analyze and compare their performance by testing them on the Grenoble University building dataset, a non-aggregated dataset that contains electricity consumption data for different types of rooms over a period of two years. Root mean squared error (RMSE) and R-squared metrics were used to evaluate the tested models. The results show that deep learning models can achieve promising results on this dataset, and that some features and preprocessing methods can improve the performance significantly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".