Weather clustering for machine learning-based hourly building energy prediction models at design phase
Bibliographic record
Abstract
• Machine learning is able to predict annual hourly building energy demands. • 82 million datasets were significantly reduced by weather clustering. • Ten weather patterns and 30 days were adequate to represent whole year. • Theoretical and implementation details of proposed clustering techniques provided for broad impacts. • ML-based hourly building energy predictions are real-time fast with acceptable accuracy. With global efforts aimed at reaching carbon neutrality by 2050, there is an increased emphasis on optimizing building energy management. Accurate hourly building energy predictions support crucial tasks such as predicting peak loads for equipment sizing, comparing energy systems, and optimization during the design phase. The main methods used to model building energy are physics-based and data-driven. The former method has been extensively studied, whereas the latter has not been thoroughly investigated. This paper investigates the advantages of using the machine learning (ML) model as a surrogate model in building engineering, specifically for predicting hourly building energy consumption during the design phase. Synthetic data is commonly used for training and testing ML models when real-life measured data is unavailable due to privacy concerns or pre-construction scenarios. However, the challenge arises from the vast dataset of synthetic data generated by combining long-term hourly meteorological data with building characteristics. Using an example building, 82 million data points were generated as a result of simulating 8,760 h when considering ten building performance parameters. To address this issue, a methodology utilizing weather clustering techniques is proposed in this work. This approach aims to reduce dataset size associated with day-by-day simulations by identifying representative weather patterns. Consequently, 7 million data points were generated by identifying ten weather patterns and selecting 30 days, with three days chosen from each cluster. The Extreme Gradient Boosting (XGBoost) algorithm is applied to develop the ML model using the condensed dataset. This model demonstrated commendable performance with testing results that are within the tolerances established by ASHRAE guideline 14. Although we used data from a residential building in Qatar, our application demonstrated that the approaches could be applied to other building types and climate zones. The developed ML model, utilizing easily accessible inputs, can predict hourly building energy consumption. It is user-friendly for non-experts, such as city developers and stakeholders, during the design and retrofit stage.
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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".