Experimental Design of Wind Speed Analytics Prediction Methodology Using Modified Deep Learning Principle

Wind speed forecasting is important in broad areas such as renewable energy management, disaster prediction, aviation safety and agricultural planning. Nevertheless, the prediction of the wind speed is a complicated problem which is highly non-linear, stochastic and chaotic and depends on a variety of environmental factors. The study introduces an enhanced deep learning-based wind speed analytics model based on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and multi-head attention models, coupled with residual connections and a specially designed peak-sensitive loss. The model was trained and validated using real-time and historical data collected with meteorological sensors and IoT-based deployments in the different geographical and climatic settings. The model showed significant progressions compared to the conventional approaches like ARIMA and bare LSTM, with a Root Mean Squared Error (RMSE) of$1.38 ~\mathrm{m} / \mathrm{s}$and a Mean Absolute Error (MAE) of$1.05 ~\mathrm{m} / \mathrm{s}$, having a R2 value of 0.95 and a maximum wind detection accuracy of 94.3%. This performance is owed to the architectural improvements that focus on the short-term changes and peak winds. Also, the hyperparameter tuning was done with Bayesian Optimization, so that optimal configuration is achieved automatically. The suggested architecture is lightweight and can be deployed on edge computing platforms, which would be appropriate to real-time forecasting. This paper has created a predictable, interpretive, and highly accurate method of predicting the wind speed. It provides access points into smart city systems and renewable energy networks, where dynamic wind situations can be anticipating to promote the optimization of efficiency and resilience.

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