Title: Machine learning model for wind direction and speed prediction

Authors: J. Gowrishankar; K. Tamilselvan; N. Sakthi Saravanan; B. Murali

Addresses: Department of Electrical and Electronics Engineering, Siddharth Institute of Engineering and Technology (Autonomous), Puttur, Andhra Pradesh, India ' Department of Electrical and Electronics Engineering, Government College of Engineering, Erode, Tamil Nadu, India ' Department of Electrical and Electronics Engineering, S.A. Engineering College, Chennai, India ' Department of Mechanical Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Tamil Nadu, India

Abstract: The capacity to estimate wind direction and speed is essential for both the generation of renewable energy and the forecasting of weather. Near the ground, the performance of the mechanistic models that are the foundation of conventional forecasting is quite low. We will explore a different data-driven strategy that is based on supervised learning. We train supervised learning algorithms utilising the previous history of wind data. We use data from individual locations and horizons to conduct a systematic comparison of a number of algorithms, during which we change the input/output variables, the amount of memory, and whether or not the model is linear or nonlinear. According to our findings, the ideal design as well as the performance of the system varies depending on the region. Our technique achieves an improvement in performance of 0.3 m/s on average when it is applied to datasets that are accessible to the public.

Keywords: machine learning model; input/output variables; linear vs. nonlinear model.

DOI: 10.1504/IJPEC.2024.140021

International Journal of Power and Energy Conversion, 2024 Vol.15 No.3, pp.208 - 219

Received: 18 Nov 2023
Accepted: 29 Dec 2023
Published online: 15 Jul 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article