Wind Power Forecasting: A Deep Learning Approach to Energy Prediction

Abstract

Accurate wind power forecasts are essential for maintaining grid stability, enhancing the efficiency of renewable energy sources, and maximizing energy production. Using time series data, this study investigates a deep learning-based method for wind energy generation prediction. The study creates predictive models that can capture intricate patterns in wind behavior by utilizing past meteorological characteristics, including temperature, humidity, and wind speed. The suggested model analyzes temporal relationships in wind power generation using sophisticated machine learning technique such as Random Forest. Performance is improved using deep learning approaches such as Gradient Boost, XGBoost, and CatBoost. The model undergoes an ensemble
process, where it compares the deep learning techniques.