WindForecastX: a dynamic approach for accurate long-term wind speed prediction in wind energy applications

Wind energy is a vital renewable energy source, and accurate Wind Speed Prediction (WSP) plays a key role in optimizing wind energy production and managing power grids effectively. However, predicting Wind Speed (WS) remains a significant challenge due to the inherently complex and dynamic behavior of wind flow. This paper introduces WindForecastX, an innovative approach that improves prediction accuracy by leveraging a dynamic unified ensemble learning model combined with advanced data assimilation techniques. The ability to accurately predict WS is vital for wind energy planning and monitoring. The accuracy of WSP has been limited because previous studies predominantly relied on data from a single location to develop models and predictions. The proposed WindForecastX model combines the strengths of ensemble learning and data assimilation techniques to enhance long-term WSPaccuracy. WindForecastX utilizes a Stacked Convolutional Neural Network (CNN) and bidirectional long short-term memory (BiLSTM) with a Data assimilation (SCBLSTM + DA) model, Adaptive Wind Speed Assimilation and Quality (AWAQ) incorporating WS observations from nearby locations. By leveraging these advanced techniques, including the Kalman filter, WindForecastX assimilates data from multiple sources to enhance the accuracy of WSP. To evaluate WindForecastX, we utilize real-world wind speed data collected from nine meteorological stations in the Tirunelveli district of Tamil Nadu, India. These stations are used for training and testing, with two stations designated as target stations for WSP. The results demonstrate that WindForecastX outperforms existing WSPmodels. Furthermore, WindForecastX exhibits reduced sensitivity to changes in the prediction time scale compared to standalone models, enhancing its reliability.