Prediction of wind energy location by parallel programming using MPI-based KMEANS clustering algorithm

Abstract

ABSTRACT Renewable energy resources, like the power of the wind, are the essential sources of energy in today’s world. To keep down the greenhouse energy degasification and stop global warming, it is very important to predict the exact location where maximum wind energy is generated. The total amount of electrical power produced by a turbine relies more on the speed of the wind, pressure created by the wind, and weather conditions. The proposed method predicts the maximum power generated in a particular location using the current conditions of the weather, pressure generated through wind, and its speed. The speed of the wind and pressure of the wind are clustered using the Message Passing Interface (MPI) based KMEANS clustering algorithm. The system minimizes the amount of time required for clustering and it is done by MPI. Clustering is formed with the help Euclidean distance of each point. The wind data is collected and formed into three clusters such as low, medium, and high based on the speed of the wind. The parameters for evaluation are considered as the speed of the wind, and the direction of the wind are determined. The results show that the speed of the wind varies up to 25 km/s with the power of 2000 watts. The proposed method compares the execution time of sequential and MPI-based KMEANS clustering for different numbers of clusters. The sequential KMEANS clustering algorithm takes 3% to 7% more time compared to MPI-based KMEANS clustering algorithm. For the maximum cluster size of 5, the MPI-based KMEANS clustering algorithm produces the result in 0.42 s.