Design and development of a data visualization platform for energy digital twins: Solar Energy potential and photovoltaic simulation

In the context of data visualization and analysis of geospatial data is crucial to define a method to integrate various tools. The objective in this thesis is to visualize solar data which can be considered like a key factor in understanding and optimizing solar energy usage. This work explores the integration of GRASS GIS, GeoServer, MapStore, and Actinia to create a streamlined workflow for managing, processing, and visualizing solar data,through a web-based mapping platform. GRASS GIS is an open-source GIS software, employed to process spatial data. The core of this project is Actinia which is a cloud-based processing engine built to integrate GRASS GIS. In fact, Actinia is used as backend thanks to its capabilities to expose geospatial functionalities via a REST API. This characteristic allows the system to perform scalable and automated geospatial processing tasks in the cloud. The processed data is then published through GeoServer, a web-based geospatial server, which provides access to the data using standard web services like WMS (Web Map Service) and WFS (Web Feature Service). Finally, a project based on MapStore, a web mapping tool built with ReactJS, is used for interactive visualization of the solar data. A critical component of this system is Docker, a set of platform as a service (PaaS) products that use OS-level virtualization to deliver software in packages called containers employed to containerize each component of the system, including GRASS GIS, Actinia, GeoServer, and MapStore. Thanks to Docker, the deployment of the entire system made easier to manage dependencies across different platforms. Ultimately, the primary aim of this project is to develop a workflow that integrates these technologies, enabling the visualization of solar data on a platform based on MapStore. The resulting solution demonstrates the advantages of using a web-based approach for geospatial data processing and visualization, providing an intuitive platform for analyzing solar energy potential across geographical areas. This integration streamlines data management and ensures that the resulting maps are accessible, interactive, and easily deployable, leveraging the scalability of cloud-based services.