Analyzing Linear Regression for Wind Speed Forecasting
AI & Decision Support System Course Research Project
Analyzed the effectiveness and limitations of Linear Regression for wind speed prediction, highlighting its role in renewable energy and weather forecasting while exploring improvements through hybrid modeling approaches.
Project Overview
Wind speed forecasting plays a crucial role in various applications, particularly in renewable energy generation, weather prediction, and environmental monitoring. This project focuses on analyzing the use of Linear Regression (LR) as a method for forecasting wind speed, exploring its effectiveness, challenges, and potential improvements.
This analysis highlights the role of Linear Regression in forecasting wind speed, evaluating its strengths, limitations, and applicability. While linear regression offers a simple and interpretable approach to wind speed prediction, its effectiveness can be limited by the complexity of wind data. By exploring other modeling techniques and combining them with linear regression, more accurate and robust predictions can be achieved, contributing to improved efficiency in wind energy production and better weather forecasting systems.