A Comparative Study of Hourly Wind Speed and Power Forecasting Using Deep Learning Networks, Weka Time Series, and ARIMA Algorithms for Smart Grid Integration
Bibliographic record
Abstract
In modern development, renewable energy is playing a crucial role for smart grid integration and in electricity demand growth as it is green and clean. Including all sources of renewable energy, wind power is particularly prevalent as it is pollution-free, cheap, and highly efficient. The main challenge that restrains the expansion of wind power utilization within the power grid is wind speed variation and uncertainties. Thus, precise wind speed forecasting is a difficult modeling approach, that greatly influences the wind power and optimal operation of the power grid. Prediction of wind speed is vital for wind power calculation and forecasting. Renewable energy forecasting is important for minimizing the power cost, scheduling energy resources, and planning maintenance. The advanced wind power forecast models help advances efficient operation and maintenance for wind turbines. This paper analytically studies the state-of-the-art approaches of wind speed forecasting regarding statistical methods (ARIMA, Weka time-series, and Deep Learning Networks), including data preprocessing, features engineering, and factors that touch prediction accuracy and modeling time. Likewise, this study provides a comparison to find the most accurate time series forecasting method based on performance evaluation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".