Net-Zero Energy Home Grows Up: Lessons and Puzzles from 10 Years of Data; Preprint
Bibliographic record
Abstract
In 2005, Habitat for Humanity of Metro Denver, with support from NREL and other partners, built one of the first homes in the US to achieve net-zero energy based on monitored data. A family of three moved into the house when it was completed and lives there still. The home has been monitored continuously for the past ten years. Although PV production has remained steady, net energy performance has varied each year. The home was a net producer of energy annually in each of the first three years and in the ninth year, but not in years four through eight. Over the years, the PV system provided between 124% and 64% of the home source energy use. Electricity use in the home increased steadily during the first eight years, even though no significant new appliance was introduced into the house, such as a window air conditioner. Miscellaneous electric loads and space heating, both strongly dependent on occupant behavior, appear to be primarily responsible for the observed increase in energy use. An interesting aspect of this case study is how, even within a single family, natural changes in occupant lifestyles over time (e.g., kids growing up, schedules changing) can substantially impact the overall energy intensity of a home. Data from the last ten years will be explored for lessons learned that can improve the way we design low-load homes without sacrificing comfort or convenience for the occupants, and how we can make realistic predictions of long-term energy performance.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".