On the Use of Indirect Measurements in Virtual Sensors for Renewable Energies: A Review
Bibliographic record
Abstract
In the dynamic landscape of renewable energy, the primary goal continues to be the enhancement of competitiveness through the implementation of cutting-edge technologies. This requires a strategic focus on reducing energy costs and maximizing system performance. Within this framework, the continuous online monitoring of assets is essential for efficient operations, by conducting measurements that describe the condition of various components. However, the execution of these measurements can present technical and economic obstacles. To overcome these challenges, the implementation of indirect measurement techniques emerges as a viable solution. By leveraging measurements obtained in easily accessible areas, these methods enable the estimation of quantities in regions that would otherwise be inaccessible. This approach improves the monitoring process’s efficiency and provides previously unattainable information. Adopting indirect measurement techniques is also cost-effective, allowing the replacement of expensive sensors with existing infrastructure, which cuts down on installation costs and labor. This paper offers a detailed state-of-the-art review by providing an in-depth examination and classification of indirect measurement techniques and virtual sensing methods applied in the field of renewable energies. It also identifies and discusses the existing challenges and limitations within this topic and explores potential future developments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".