Intelligent Forecasting of Hybrid Solar–Wind Renewable Energy Systems Using Machine Learning Techniques in Python

Abstract

Hybrid renewable energy forecasting has emerged as a critical research area due to the intermittent and location-dependent nature of individual energy resources. Solar power generation is highly dependent on sunlight availability and is significantly affected by cloudy and rainy conditions. Similarly, wind energy generation requires sustained wind speeds, which vary geographically. In India, wind resources are concentrated in specific regions such as Gujarat, Rajasthan, and Tamil Nadu. Considering the variability and limitations of standalone renewable sources, this study proposes a hybrid energy forecasting model that integrates both solar and wind energy systems to improve prediction accuracy and reliability. The objective is to forecast total power generation by leveraging complementary characteristics of solar and wind resources.A Linear Regression-based machine learning approach is employed to model the relationship between power output (measured in kW or MW) as the dependent variable and multiple environmental and meteorological parameters as independent variables. The selected features include solar irradiance, temperature, humidity, cloud cover, Air Quality Index (AQI), wind speed (m/s), wind direction, and air density. The model is implemented using Python in a VS Code development environment. By combining diverse environmental factors into a unified predictive framework, the proposed hybrid forecasting model aims to enhance energy estimation accuracy and support more efficient renewable energy management. This approach contributes to improved planning, grid stability, and optimal utilization of renewable resources in the transition toward sustainable energy systems.