Optimizing Wind Energy Generation: Wind Speed Forecasting Using Elman Recurrent Neural Networks for Enhanced Power Generation in Turbines

Anthony Brandon (College of Engineering, Iowa State University of Science and Technology, Ames, USA)
Richrad Xavier (College of Engineering, Iowa State University of Science and Technology, Ames, USA)
Cley Valentine Ludwig (College of Engineering, Iowa State University of Science and Technology, Ames, USA)

Vertex
Vol. 13 No. 1 (2023): December: Engineering

This research delves into the realm of wind energy by exploring the accuracy of wind speed forecasting using the Elman Recurrent Neural Network (RNN) and its direct influence on the power generation of wind turbines. Leveraging historical wind speed data and employing the Elman RNN, this study demonstrates the model's precision in forecasting wind speeds, capturing temporal dependencies, and elucidating their impact on electricity output. Correlating these forecasts with actual power generation records, the research establishes a profound relationship, showcasing how even minor variations in predicted wind speeds significantly influence the amount of electricity produced by wind turbines. The study's findings underscore the critical role of accurate wind speed predictions in optimizing wind farm operations, enhancing energy capture efficiency, and contributing to grid stability. Furthermore, the research sets the stage for practical applications in renewable energy planning and policy-making, offering insights that shape the future trajectory of wind energy utilization. The research concludes by proposing avenues for further refinement in predictive models, real-time integration strategies, and long-term forecasting, guiding the path towards a sustainable and resilient energy landscape.