Application of Novel Strategy Based on Deep Learning for Forecasting Wind Speed - S. P., Ananadaraj; Srivastava, Manish; Singh, Harjinder; Jameel, Roshan (International Journal of Intelligent Systems and Applications in Engineering, 2023)
In several fields, including aviation, environmental management, and the production of energy from renewable sources, exact wind speed forecast is important. The complexity and nonlinearity of the data on wind speed provide difficulties for conventional prediction techniques frequently. In this study, a unique method for improving predictions of wind speed is put forth. It combines the optimization of Satin Bowerbird (SBO) algorithm with the Backpropagation Neural Network (BPNN). The BPNN is ideal for modeling wind speed patterns because it can capture nonlinear interactions. However, the conventional BPNN training procedure is prone to become stuck in local optima, producing predictions that are less than ideal. The SBO technique is used to modify the BPNN characteristics in order to get around this restriction. The performance of the suggested strategy is examined in this study using wind speed information. The suggested SBO-BPNN methodology has the potential to increase the accuracy of wind speed predictions, allowing for better scheduling and making choices in a variety of wind energy-dependent businesses. Future studies should examine how well this model works for similar prediction-related tasks like estimating wind power or predicting wind direction.