Multivariate analysis of energy and solar performance across Dubai: insights from MANOVA and cluster analysis

Solar energy adoption became a key component in achieving the UAE's sustainability strategy, featuring the abundance of solar irradiance in the region that tends to reduce the dependence on carbon-based resources through solar energy integration. However, despite the UAE's solar energy adoption efforts, there is a clear gap due to the limited statistical analysis to evaluate the performance of such solar implementation across Dubai's diverse community areas for the different building types. This study aims to investigate the performance of energy consumption and solar generation under the Shams Dubai program, specifically across the residential, commercial, and industrial sectors within various communities in Dubai. The study analyzed 93 community areas using hierarchal clustering Analysis and Multivariate Analysis of Variance to group and compare energy patterns. The clustering analysis identified three clustered groups that differ in building types and area, influencing energy consumption and solar energy generated, causing energy and solar pattern disparities. The MANOVA results confirmed a statistically significant difference with a 95% confidence interval across the three clusters. Accordingly, this study offers actionable insights for utility companies and policymakers to prioritize large-scale solar projects in high-demand commercial areas while focusing on underperforming residential areas by conducting awareness and incentive campaigns to enhance solar adoption. The study's results enable more informed resource allocation, support progress toward Dubai's solar adoption targets, and aid in developing tailored data-driven decisions for energy efficiency improvements. By translating statistical insights into practical strategies, this research allows decision-makers to reduce carbon emissions, optimize solar energy, and achieve sustainability objectives based on Dubai's rapid urbanization context.