Climate Change Impacts on Residential and Commercial Loads in the Western U.S. Grid
Bibliographic record
Abstract
This paper presents a multidisciplinary modeling approach to quickly quantify climate change impacts on energy consumption, peak load, and load composition of residential and commercial buildings. This research focuses on addressing the impact of temperature changes on the building cooling load in ten major cities across the Western United States and Canada. Our results have shown that by the mid-century, building yearly energy consumption and peak load will increase in the Southwest. Moreover, the peak load months will spread out to not only the summer months but also spring and autumn months. The Pacific Northwest will experience more hot days in the summer months. The penetration levels of air-conditioning (a/c) systems in this region are likely to increase significantly over the years. As a result, some locations in the Pacific Northwest may be shifted from winter peaking to summer peaking. Overall, the Western U.S. grid may see more simultaneous peaks across the North and South in summer months. Increased cooling load will result in a significant increase in the motor load, which consumes more reactive power and requires stronger voltage support from the grid. This study suggests an increasing need for the industry to implement new technology to increase the efficiency of temperature-sensitive loads and apply proper protection and control to prevent possible adverse impacts of a/c motor loads.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".