Addressing Explainability in Load Forecasting Using Time Series Machine Learning Models
Bibliographic record
Abstract
Energy management is a crucial issue in the modern world, as it affects various aspects of human life and the environment. It is a complex and challenging task that involves multiple factors and uncertainties. Machine learning has been widely adopted for improving building energy efficiency and flexibility in the past decade, as it can leverage the massive building operational data to provide accurate and reliable predictions and recommendations. However, with the increasing complexity, machine learning models are becoming black-boxes that are difficult to understand and trust by end-users. Hence, Explainable AI (XAI) has gained significant popularity in last years. In this paper, we focus on the explainability of different machine learning methods with regards to electricity consumption prediction. We apply explainability methods to interpret the results of these models and to provide insights into the factors that affect the electricity consumption. We use 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. We evaluate the performance and the interpretability of the machine learning models and we discuss the implications and the limitations of our approach.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".