Structure dependent weather normalization
Bibliographic record
Abstract
Abstract In this paper, we introduce a new analytical method to normalize and forecast the energy usage/loss of residential and commercial buildings. Weather conditions have large effects on energy and economic activity. Weather Normalization is an important step in building energy rating and retrofit measurements. It has also become increasingly important because of changes in the worlds weather patterns due to global warming. Accounting for the impacts of weather on energy use in buildings is an extremely exhaustive challenge because of the complexity and diversity in the operation of the mechanical and electrical systems. In traditional weather normalization methods some building parameters, such as building size, window size, construction joints, and the effect of flues, are missing. We present a Structure Dependent Energy Usage/Loss (SDE U/L) linear and nonlinear models by using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) to capture and forecast the behavior of energy consumption/loss. This model considers different building and weather parameters. Using the (SDE U/L) model, we present an innovative approach for linear and nonlinear weather normalization. Our simulation results demonstrate the flexibility and advantages of our structure dependent weather normalization method. Unlike most existing methods, the (SDE U/L) method does not impose any constraints on a property on its property type, use details, and energy data to be able to perform weather normalization for any building over time.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".