Spatio-Temporal Fusion Model for Wind Speed Prediction using IOT Sensor Grids and Graph Neural Networks

P. Anusha; D. Sheksha Vali

Spatio-Temporal Fusion Model for Wind Speed Prediction using IOT Sensor Grids and Graph Neural Networks

Author(s)	P. Anusha, D. Sheksha Vali
Country	India
Abstract	Prediction of wind speed is not an easy task because of the uncertainty and extreme changes in the wind behavior. This paper creates a graph-based spatiotemporal deep learning model that can identify rich spatial and time-based patterns in the data supplied by wind speed and wind direction measurements on the nearby wind farms. The wind farm system is represented as an unweighted undirected graph in which the nodes are the different wind locations. It presents a scalable graph convolutional deep learning model based on a localized first-order spectral approximation that is used to represent spatial dynamics, and Long Short-term Memory (LSTM) based networks at every node model temporal dynamics. The framework presented collaboratively gains knowledge of spatial interaction and temporal variations among various wind locations. In order to provide additional uncertainty-resistance, rough set theory is added with upper and lower corresponding parameter estimates, which allow extraction of latent features based on an interval. It is shown through experimental studies that the suggested method better projects the complex spatiotemporal characteristics of winds and provides better forecasting performance than up-to-date deep learning and shallow models.
Keywords	Wind speed prediction, Deep learning, Graph convolutional networks, LSTM, Spatiotemporal modeling, Rough set theory.
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-06-04

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Spatio-Temporal Fusion Model for Wind Speed Prediction using IOT Sensor Grids and Graph Neural Networks

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