Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis

G. Ponkumar, S. Jayaprakash, K. Karthick

Energies 2023 article cited by 57

Source Open-access PDF More in Climate & Environment

importance 6/10 ai assisted Energy Load and Power Forecasting grid forecasting stage: research open-source model

Problem

Wind power output is hard to predict because wind speeds fluctuate, complicating grid operation, unit commitment, and turbine maintenance planning.

Why it matters

Forecast accuracy directly affects unit commitment, maintenance scheduling, and power traders' profits.

What the AI did

Built a very-short-term forecasting model comparing LightGBM, random forest, CatBoost, and XGBoost using 16 selected wind-system parameters, assessed with MAE and MSE.

Results

not stated in source

Impact

Evidence supports development and comparative evaluation of ML forecasting models only; no numeric performance shown.

Record

Venue	Energies
Published	2023
Work type	article
Authors	G. Ponkumar, S. Jayaprakash, K. Karthick
Citations	57 (OpenAlex, at import time)
References	42
Model or method	LightGBM, random forest, CatBoost, XGBoost
Organization	not stated in source
Primary source	https://doi.org/10.3390/en16145459
Open access	gold
Keywords	wind power forecasting, machine learning, LightGBM, XGBoost, very-short-term, grid operation

Importance rating

6 / 10

significant — thousands to millions affected

Abstract states wind power forecasting is crucial for unit commitment, maintenance scheduling, trader profits, and efficient grid operation, affecting power system integration at industry scale.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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