Predictive Spatial Analytics of Wave Energy Converters Based on Image Representation and Convolutional Neural Networks
Bibliographic record
Abstract
Climate change is currently the main global concern that is caused by increasing greenhouse gas emissions, and severe environmental challenges worldwide. To overcome this challenge, the global adoption to fully renewable energy usage is on the progress. Wave Energy Converters (WECs) are one of these technologies that can harness the power of ocean waves to generate clean, and renewable energy. Wave Energy Converter help reduce reliance on fossil fuels, contributing to a reduction in carbon emissions and supporting efforts to mitigate climate change. Consequently, the more these WECs generate power, the higher share of produced energy will be clean, without any significant emissions. To this end, an innovative optimal spatial coordination model is presented and applied WECs, in this paper. The proposed model consists of two main segments. Firstly, the spatial data, and output power of WECs are combined with each other, and converted to an image. Then, a 2D Convolutional Neural Netowrk (CNN) model analyzes the image to predicted final output power. Based on the predicted output power, the model suggests the optimal X,Y coordination of the WECs to achieve the propose of maximum electrical power generation. The proposed model is evaluated against several Key Performance Indicators (KPIs) with high accuracy results, and the least errors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".