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

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.

KEYWORDS:

• Parallel clustering
• message passing interface
• wind power prediction
• wind pressure
• wind direction
• weather condition
• KMEANS clustering algorithm

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No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported that there is no funding associated with the work featured in this article.

Notes on contributors

Priya Ponnuswamy

P. Priya Ponnusamy hold a bachelor’s degree with a major in computer Science and Engineering awarded by Bharathiyar University in 2001 and completed Master’s degree in Information Technology from Anna University of Technology, Coimbatore in 2009. Since 2017 she is working as a Assistant Professor selection grade in PSG Institute of Technology and Applied Research . She is completed Ph.D in the area of Cloud Computing and her research Interests are Parallel Computing, Computer Networks and Cloud Computing.

Shabariram Chokkalingam Palaniappan

Shabariram C. P. received the BE and ME degrees in Computer Science and Engineering from the Anna University in May 2014. His current research interests include cloud computing, edge computing, biological data analysis and fault localization. He has written 2 books and published conference papers and journal articles on various aspects of cloud resource scheduling, DNA sequence analysis and software quality.