Long-term performance and GHG emission offset analysis of small-scale grid-tied residential solar PV systems in northerly latitudes
Bibliographic record
Abstract
The integration of solar energy systems into residential buildings is an emerging trend worldwide and is an important method of mitigating the impact of housing on greenhouse gas (GHG) emissions. To achieve optimal energy performance, particularly in cold-climate regions, the generating capacity of solar photovoltaic systems (PVs) as well as their corresponding GHG emissions offsets must be investigated. In the present paper the energy generation of 86 PV sites in northerly latitudes is analysed to investigate their actual long-term performance considering various parameters. Energy payback time (EPBT) and GHG emissions of the monitored PV systems are also investigated and key parameters influencing both EPBT and GHG emissions are identified. Results indicate that there is a correlation between a solar PV layout setting and its EPBT and GHG emissions. Other results include the solar PV potential benchmarking in each of the cities where the study is conducted and the recommended layout placement in order to maximise the annual energy aggregate of PV systems and thus minimize their EPBT and GHG emissions.
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 | 0.000 |
| 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) | 0.001 | 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".