Algorithm in geo-spatial distribution and mapping of distributed renewable energy systems in Zimbabwe

Abstract

The rapid adoption of renewable energy is crucial for achieving sustainable development and addressing the growing energy needs of  developing nations like Zimbabwe. This project focused on developing a geospatial application to analyze and visualize the distribution of  renewable energy systems across Zimbabwe. By integrating a comprehensive dataset of solar, wind, and micro-hydro power stations,  the application offers an interactive map that provides detailed insights into the spatial distribution of these energy sources. Key  functionalities of the application include hotspot analysis to identify regions with high concentrations of renewable energy capacity,  clustering analysis to uncover patterns and optimize the placement of new installations, and network analysis to assess the connectivity  and efficiency of energy distribution networks. The application was developed using Python and various libraries such as Pandas,  GeoPandas, Folium, and NetworkX, and features a userfriendly graphical user interface (GUI) for ease of use. The system underwent  rigorous testing, including unit testing, integration testing, and system evaluation, to ensure its accuracy, reliability, and performance.  The results demonstrated that the application met the specified requirements and performed effectively in real-world scenarios. The  analysis revealed significant insights that can aid policymakers, stakeholders, and researchers in making informed decisions to enhance  the planning and management of renewable energy resources in Zimbabwe. This research underscores the potential of geospatial  analysis in promoting sustainable energy development. By leveraging data-driven insights, it facilitates targeted interventions and  optimized resource allocation, contributing to the broader goal of achieving energy sustainability and addressing climate change  challenges. Future work can expand the system's capabilities by integrating real-time data, advanced analytics, and broader geographic  coverage, further enhancing its utility and impact.