Leveraging Deep Learning Architectures for Accurate Wind Speed and Power Prediction in Renewable Energy Systems

The advanced deep learning models that are incorporated in this paper present strategies on the forecasting of Wind velocity and wind power. While metaheuristic strategies do not define the system and work to establish that the given inputs and outputs can be linked, deep learning models render a higher capacity to capture temporal and spatial dependencies in wind data. It is expected that through dealing with big data to recognize complex relations, the improvements in wind speed and power prediction would be immensely increased. Thus, this approach allows for more accurate and specific forecasting favoring the efficient functioning of wind energy and its incorporation into the electricity grid. The performance of these deep learning models is tested through New Delhi, India specific case incrementally showing that these models are very computationally efficient and have better prediction capabilities.