Optimal location selection for a distributed hybrid renewable energy system in rural Western Australia: A data mining approach

The adverse effects of coal and gas energy production with the subsequent rapid increase in energy consumption emphasize the importance for Australia to adopt more renewable energy sources to counteract these dismissive contributions to climate change. This work presents a data mining approach for optimally selecting the best locations for installing a distributed hybrid renewable energy generation system for rural regions in Western Australia. The K-Means and K-Medoids clustering algorithms were used to divide the constructed dataset into clusters. In total, 69 locations were selected for the overall dataset, proceeding with the filtering process. The returned cluster data were graphically rendered on a Western Australia map for the region. The clustering algorithms were evaluated using the Dunn index, such that K-Means performed to a higher degree than K-Medoids, given our dataset's nature. After passing the generated clusters to HOMER software to generate the potential wind and solar energy output for each centroid, K-Medoids produced a set of locations that generated higher solar and wind energy on average. However, due to the reduced internal validation, K-Medoids might not be as valuable as K-Means, it does not cluster the data points very well, and within-cluster location energy requirements are not considered in our study.

Automated summary

This study presents a data mining approach for selecting the optimal locations for a distributed hybrid renewable energy generation system in rural regions of Western Australia. The K-Means and K-Medoids clustering algorithms were used to divide the dataset into clusters.