Reducing electricity demand and enhancing heat supply flexibility through energy efficiency and district heating
Bibliographic record
Abstract
Electrification is the main approach for decarbonizing buildings’ heating supply, which is still dominated by the use of fossil fuels. However, electrification using inefficient heating technologies and without improvements in buildings’ energy efficiency can considerably increase the strain on the power grid and limit the decarbonization of other sectors, such as transport and industry. This study focuses on Norway, where direct electric heating is the dominant heating technology, leading to high peak electricity demands in the winter and little flexible heat supply. The study looks at the combination of improved energy efficiency standards for buildings together with increased use of waterborne heating systems with district heating and heat pumps, and demonstrates potential reductions in total and peak demands for electricity. In the most ambitious scenarios, combining high energy efficiency standards with maximal use of district heating in urban areas and heat pumps in rural areas could allow 26% reduction in total electricity demand, and up to 35% reduction in the peak electricity demand for buildings within 2050 compared to 2020 level. This corresponds to a reduction of 17% in total electricity demand and 38% in peak power demand in 2050 compared to a business-as-usual scenario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".