Analysis of the Wind Potential in the Mexican Republic and Prediction of Its Behavior through Dense Neural Networks

Mario Molina-Almaraz; Luis O. Solís-Sánchez; Celina Lizeth Castañeda-Miranda; Luis E. Bañuelos-García; Eduardo García-Sánchez; Héctor A. Guerrero-Osuna

doi:10.20944/preprints202410.0863.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 10 October 2024 / Approved: 11 October 2024 / Online: 14 October 2024 (03:40:09 CEST)

Climate change is a global issue that has driven the adoption of renewable energies due to their sustainability and environmental benefits. However, these energies face limitations, such as the lack of regional studies on wind or solar dynamics and the efficiency of energy systems. Tools that simulate and calculate energy potential while considering uncontrollable climatic variables are crucial for optimizing the design of these systems. Artificial intelligence, particularly multilayer neural networks, has proven effective in data prediction across industries. This paper focuses on training a 3-layer neural network using the ReLU activation function and quadratic error to predict wind potential density in Mexico, aiming to identify key areas for renewable energy development.

Artificial Intelligence (AI); neural network; perceptron; renewable energy; Wind Power Density (WPD)

Environmental and Earth Sciences, Atmospheric Science and Meteorology

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Analysis of the Wind Potential in the Mexican Republic and Prediction of Its Behavior through Dense Neural Networks

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