Using machine learning methods for long-term technical and economic evaluation of wind power plants
Bibliographic record
Abstract
The depletion of hydrocarbon reserves and the impact of global warming have posed significant challenges to the continued use of fossil fuels. Consequently, renewable energy sources have garnered substantial attention, with some countries now deriving a significant portion of their total energy needs from these alternatives. Among renewable sources, wind energy has been recognized as one of the most accessible and clean. However, it is imperative to evaluate wind power plants both technically and economically. This involves calculating the levelized cost of energy in comparison to fossil-based energy sources and predicting the minimum and maximum energy output over the long term. Achieving this requires long-term forecasts of wind speeds at specific locations, which involve complex mathematical modeling and computations typically performed by supercomputers. In this study, a data-driven machine learning model has been employed to predict wind speeds in Calgary over a 25-year period with minimal CPU time. Throughout the power plant's operational life, the optimal model was also used to calculate the annual energy production. The hybrid CNN-LSTM model demonstrated superior accuracy based on model accuracy metrics. Consequently, the levelized cost of energy produced by the plant was calculated at $0.09 per kWh, which is competitive within the Canadian electricity market. The investment reached a breakeven point in approximately six years, which is deemed acceptable. • Constructing hybrid ML model for long-term wind energy prediction. • ML prediction guides feasibility of power plant. • ML + Tech Data guides economic feasibility.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".