Analysis of Wind Speed: A Fourier Neural Network Approach

This work is motivated by the urgent need to transition towards renewable energy resources, the use of which is preponderant to mitigate environmental and climate threats that result from the growing energy demand and continued dependence on fossil fuels.

The primary focus on wind speed is recognized for its highly efficient and environmentally friendly characteristics, as the efficient generation of electricity through wind turbines is significantly influenced by windspeed, making its accurate prediction a vital component in the design process. However, the existing lack of comprehensive and precise wind speed data brings a formidable challenge from the engineering perspective. To address this challenge, an innovative solution is presented in this work, which considers a Fourier neural network, proving to accurately analyze and predict wind speed data, to an acceptable extent.

The proposed method proves to be advantageous, especially when a windspeed sensor fails at a specific time frame, as it can generate a portion of the missing information by using the previous measurements of windspeed. This approach demonstrates its potential to enhance the reliability and accuracy of wind speed prediction, which is essential for effective energy generation and infrastructure planning.

In this study, a thorough validation of the proposed technique is carried out by meticulously comparing real-world windspeed data with predictions generated by the Fourier neural network. The results affirm the effectiveness and applicability of the proposed approach, reinforcing its potential to revolutionize windspeed data prediction, and advance the utilization of renewable energy sources to fight climate change.