Potential analysis and energy prediction of photovoltaic power plants using satellite-based remote sensing and artificial intelligence techniques
Bibliographic record
Abstract
Photovoltaic (PV) systems have seen significant global growth due to their economic and environmental benefits. However, the output of PV systems is subject to uncertainties arising from factors like unpredictable weather conditions. Given the considerable uncertainty in meteorological data, Geographic Information Systems (GIS) have emerged as effective tools for analyzing such data. This study presents a novel method based on satellite-based remote sensing and artificial intelligence techniques to assess the potential of PV power plants and predict energy generation in different locations. We utilize GIS and the Analytic Hierarchy Process (AHP) in ArcGIS software to evaluate suitable sites for PV systems. Satellite data from global sources is used to analyze PV energy production based on specific geographic coordinates. Several machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), Decision Tree (DT), and XGBoost, are applied to predict PV energy production from meteorological variables. The evaluation, using various statistical metrics, shows that the XGBoost algorithm outperforms others, achieving up to 91% accuracy in predicting energy production from PV systems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".