Data Analytics for Forecasting of Texas Wind Turbines Generated Power

The utilization of renewable energy sources, par-ticularly wind power, has become increasingly significant in contemporary energy systems. This study focuses on applying data analytics and visualization techniques to forecast the gen-erated power of a wind turbine in Texas. The initial phase includes gathering data on parameters such as wind speed, turbine specifications, and historical power generation figures. The collected data undergoes cleaning procedures to address outliers, ensuring the integrity and reliability of subsequent analyses. Subsequently, detailed data analysis is conducted, incor-porating correlation analysis techniques. Scatter plots visualize the relationship between wind speed and power generation, pro-viding insights into the dynamics of wind turbine performance. Pearson correlation coefficients are calculated to quantify the strength and direction of these relationships. Additionally, a correlation matrix is constructed to assess the interrelationships between different variables, aiding in understanding the complex interactions influencing power generation. Regression curves are fitted to the data to model the relationship between wind speed and power output, enabling the formulation of predictive models for future power generation. Pair plots visualize the correlations between multiple variables simultaneously, offering a comprehensive overview of the underlying patterns and trends.