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Related Concept Videos

Wind Turbine Machine Models01:24

Wind Turbine Machine Models

In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Precipitation Processes

The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Precipitation and Co-precipitation01:17

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Related Experiment Videos

Enhancing wind and solar energy forecasting through time-series feature engineering and ensemble machine learning.

Nouf Abd Elmunim1, Mohamed Arbi Khlifi2, Murdhy A Aldawsari3

  • 1Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Scientific Reports
|May 19, 2026
PubMed
Summary

Accurate renewable energy forecasting is crucial for grid stability. Advanced machine learning and deep learning models, incorporating engineered features, significantly outperform traditional methods for wind and solar power prediction.

Keywords:
CatBoostEnergy analyticsEnergy production forecastingEnsemble regressionHourly predictionMachine learningRenewable energyTime series features

Related Experiment Videos

Area of Science:

  • Energy Systems Engineering
  • Data Science
  • Machine Learning

Background:

  • Accurate forecasting of renewable energy generation is vital for grid stability and operational planning.
  • Existing methods often struggle with the inherent variability of wind and solar power.

Purpose of the Study:

  • To develop and evaluate a comprehensive time-series forecasting framework for wind and solar power.
  • To compare the performance of advanced machine learning and deep learning models against statistical methods.

Main Methods:

  • A framework integrating lagged variables, rolling statistics, calendar features, and temporal encodings was developed.
  • Expanding-window time-series cross-validation was used for robust evaluation.
  • Models evaluated include ARIMA, XGBoost, LightGBM, CatBoost, and LSTM on a multi-year dataset.

Main Results:

  • Ensemble learning and deep neural models consistently outperformed statistical methods.
  • LightGBM and LSTM models showed strong performance for short-term wind and solar forecasting.
  • Feature importance analysis highlighted the significance of lagged production and rolling-window statistics.

Conclusions:

  • The proposed framework provides a reproducible benchmark for renewable energy forecasting.
  • Advanced models offer improved accuracy for operational decision-making in renewable energy integration.
  • Forecasting accuracy decreases with longer prediction horizons, indicating increased uncertainty.