The Role of Earth Observation Satellites in Maximizing Renewable Energy Production: Case Studies Analysis for Renewable Power Plants
Bibliographic record
Abstract
This paper is based on a novel approach towards clean energy production, i.e., space innovative applications toward sustainable development. Specifically, the role of Earth observation (EO) satellites in maximizing renewable energy production is considered to show the enormous potential in exploiting sustainable energy generation plants when the Earth is mapped by satellites to provide some peculiar parameters (e.g., solar irradiance, wind speed, precipitation, climate conditions, geothermal data). In this framework, RETScreen clean energy management software can be used for numerical analysis, such as energy generation and efficiency, prices, emission reductions, financial viability and hazard of various types of renewable-energy and energy-efficient technologies (RETs), based on a large database of satellite parameters. This simplifies initial assessments and provides streamlined processes that enable funders, architects, designers, regulators, etc. to make decisions on future clean energy initiatives. After describing the logic of life cycle analysis of RETScreen, two case studies (Mexicali and Toronto) on multiple technologies power plant are analyzed. The different results obtained, when projecting the two scenarios, showed how the software could be useful in the pre-feasibility phase to discriminate the type of installation not efficient for the selected location or not convenient in terms of internal rate of return (IRR) on equity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".