Optimizing Renewable Energy Site Selection in Rural Australia: Clustering Algorithms and Energy Potential Analysis

Renewable energy development is a critical issue in Australia, and identifying suitable regions for constructing renewable energy plants is an essential step towards achieving sustainable energy goals. This work presents insights and techniques aimed at identifying optimal locations for renewable energy stations in rural areas across Australia as a whole. Various clustering algorithms were employed in line with our methodology, namely K-Means, DBSCAN, Hierarchical clustering, and K-Medoids. Each algorithm generated clusters, facilitating the identification of appropriate regions based on a range of data attributes. A genetic algorithm was integrated into an iterative process to identify the most appropriate clustering method. Additionally, we employed the HOMER Pro software to process the generated cluster centers and estimate the solar and wind energy potential for each location. The energy output data obtained from HOMER Pro is a critical factor in evaluating the efficacy of each algorithm's solutions. Our analysis shows new insights into the potential of clustering techniques for identifying suitable locations, as well as limitations and challenges that need to be addressed. Our results have implications for other stakeholders interested in promoting renewable energy development in Australia. Overall, contribution of this study is focusing on renewable energy development in Australia by presenting new insights and techniques that can inform future research and policy decisions.