Data Driven, Integrated and Inclusive Energy Planning with EAE

File 1 contains an outline of the course, its learning objectives, duration, example assessment methods, and the IT requirements. In File 2, an example timetable is provided that breaks the course into two different blocks and suggests time allocations. File 3 provides a reading list containing key publications, the EAE data and methods, and other related interesting publications.

Week 1 lecture introduces the importance of energy planning for socio‑economic development and highlights current global energy challenges. It explains why integrated and inclusive planning—supported by geospatial data and tools—is essential for expanding clean energy solutions. The session also presents the ecosystem of geospatial tools, with a focus on EAE, and guides learners on creating and using a MyEAE account for data‑driven energy planning.

Week 2 lecture introduces EAE as an open-source geospatial platform that identifies priority areas for energy intervention by integrating data on energy demand, supply, infrastructure, and socio-economic factors. It highlights EAE’s key uses, datasets, and its multi criteria analysis framework, which supports data driven, inclusive planning and investment decisions on energy.

Week 3 lecture introduces the core concepts of GIS, covering vector, raster, and tabular geospatial data types and how they are used in energy planning. It explains essential data cleaning, attribute management, and coordinate reference system adjustments, and demonstrates key preprocessing steps—such as simplifying, dissolving, clipping, and resampling data—needed to prepare datasets for use in EAE.

Week 4 lecture introduces the EAE front end interface, demonstrating how users can visualize geospatial data, explore datasets, and conduct multi criteria spatial analysis without needing GIS expertise. It explains key functions such as managing layers, applying filters, generating analytical indices, and exporting results, enabling users to identify high priority areas for energy interventions and create reports using the platform’s built-in tools.

Week 5 lecture explains the structure and functionalities of the EAE back‑end, highlighting its automated data processing, dynamic database system, and modular API. It introduces the CMS, through which administrators can organize categories, geographies, and datasets, upload and configure data, and manage metadata. The back‑end enables efficient, user‑friendly data integration, making EAE accessible even to users without GIS or programming expertise.

Week 6 lecture explains the meaning and importance of data governance in the energy sector, highlighting how effective governance ensures data quality, security, availability, and accountability. It outlines how EAE implements strong governance through data standards, validation, metadata practices, working groups, and capacity building. It also introduces sustainability strategies that support long‑term adoption and management of EAE within countries.