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Short-term Weather Forecasting for Wind Energy Generation using A Deep Learning Technique

Inadequate or erroneous weather predictions have a great impact on wind turbine energy output. Certain weather fluctuations and interruptions affect the efficient operation of wind turbines. Short-term weather predictions that are calibrated and verified make it simple to reduce the threat presented by meteorological uncertainty. Conventional physics-based techniques for forecasting are relatively weaker than data-driven approaches since wind turbine components usually have a multipart nonlinear relationship. Compared to physical methods, non-parametric alternatives like deep learning are thought to be more stable and lead to fewer mistakes in analyst predictions. This research presents the state-of-the-art Temporal Convolutional Network (TCN), one of the deep learning methods, which is introduced as a means of enhancing the precision of short-term forecasting of weather variables. TCN is an architecture that makes use of casual convolutions and dilations in order to be adaptable for sequential data with its temporality and huge receptive fields. Seven significant meteorological parameters from the NASA POWER dataset of Chouchala Sea Beach in Chittagong, Bangladesh, over a period of four months were used in this research. The accuracy was measured using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results obtained from the suggested TCN model demonstrate that the predicted outcome is pretty accurate and consistent.