Evaluation of economic feasibility of rooftop solar energy systems under multiple variables
Bibliographic record
Abstract
Rooftop harvesting of solar energy is a promising method to provide a great portion of household energy requirements in many parts of the world. However, the cost of solar energy systems sometimes makes the exploration of rooftop solar energy systems not attractive to property owners. This study evaluates the economic factors that could affect the decision on whether to consider the installation of solar energy systems using the estimated time that the cumulative solar savings would become positive. The economic implication of increasing the micro-generation capacity of individual households, and the impact of varied interest rates, and subsidies were also evaluated. Among the three factors that were presented, the result showed that increasing the amount of electricity that is allowed to be generated from individual rooftops will result in the highest economic attractiveness for end-users. This is also expected to move the world closer to the goal of sustainable management of non-renewable resources for present and future generations. Increasing the micro-generation capacity of electricity from photovoltaic (PV) rooftops by individual households without increasing the electricity distribution fees results in a reduction of the time to reach positive solar savings. In addition, increasing the micro-generation capacity of electricity from PV rooftops is expected to contribute to a reduction in the greenhouse gas (GHG) emissions from the electricity grid for the entire community. This study recommends the encouragement of policies that allow for the maximization of electricity generation potential from rooftops of residential and industrial buildings.
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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 | 0.004 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".