HALO: HVAC Load Forecasting With Industrial IoT and Local–Global-Scale Transformer
Bibliographic record
Abstract
The evolution of Internet-of-Things (IoT) is fostering the use of intelligent controls for energy conservation. Yet, the efficacy of these strategies is largely tied to diverse load forecasting algorithms. Given the significant contribution of heating, ventilation, and air-conditioning (HVAC) systems to global energy consumption, accurate forecasting of HVAC power usage is crucial for improving overall energy efficiency. However, real-world HVAC load forecasting, bolstered by various IoT devices, is complicated by multiple factors: data variability, power load fluctuations, electronic phenomena (e.g., zero drifts), and the increased time complexity and larger model sizes required to manage accumulating historical data. To address these challenges, we first present an in-depth measurement study on the characteristics of HVAC load at a minute scale based on HVAC data collected in six locations. We propose HALO, a transformer-based framework specifically designed for forecasting HVAC load. HALO incorporates an adaptive data pre-processing stage and a local-global-scale transformer-based load forecasting stage, enabling precise forecasting of HVAC load and optimization of energy utilization. Evaluation based on real-world data traces from a prototype application demonstrates that the proposed framework significantly outperforms existing models.
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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 | 0.001 |
| 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".