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Analyzing Wind Speed Data through Markov Chain based Profiling and Clustering
Conference proceeding

Analyzing Wind Speed Data through Markov Chain based Profiling and Clustering

Jeremiah D. Deng, Hsin-Shyuan Lee, Cameron McMillan, Aysha Rimoni and Ming Zhang
Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, Vol.2-, pp.67-67
MLSDA'14: Machine Learning for Sensory Data Analysis
ACM Other Conferences
02/12/2014
DOI: https://doi.org/10.1145/2689746.2689756
Handle:
https://hdl.handle.net/10523/39584

Abstract

Computing methodologies -- Machine learning Computing methodologies -- Machine learning -- Learning paradigms -- Unsupervised learning -- Cluster analysis
Markov chain models have been a popular tool used for wind speed data modelling. This paper presents a novel approach of assessing similarity between Markov chain models, hence enabling cluster analysis of wind speed profiles based on Markov chains. We experiment with real-world wind speed data and construct weekly and monthly Markov chain models for clustering, visualization, and anomaly detection. Some preliminary but promising results have been obtained.

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