Data-driven Decision Support for Renewable Energy and Infrastructure Development: GIS-based Approach

Offentlig prosjektsammendrag

Among the renewable energy sources, solar and wind are rapidly becoming popular for being inexhaustible, clean, and dependable. Meanwhile, power conversion efficiency for renewable energy has improved with great technological leaps. Following these trends, solar and wind will become more affordable in years to come and considerable investments are to be expected. As solar and wind plants are characterized by their high site flexibility, the site selection procedure is a crucial factor for their efficiency and financial viability. Many aspects affect site selection, amongst them; legal, environmental, technical, and financial. Today, information gathering for site selection assessments is a manual and time-consuming process. Especially when considering the emerging opportunities related to combining renewables with existing business infrastructure, e.g., floating solar with salmon farming, the complexity of factors to assess will increase substantially. To effectively evaluate land suitability for the optimal placement of solar and wind energy, implementation of Geographic Information Systems (GIS), remote sensing techniques, and multi-criteria decision methods (MCDA) will have the potential to accelerate renewable energy and infrastructure development through enhanced data insight. The research in this project is based on multi-criteria site selection research and is investigating an innovative data-driven approach to extract information from satellite imagery to enhance the multi-criteria analysis. Enernite’s innovation involves streamlining the site assessment process for energy and infrastructure developers through applying state-of-the-art deep learning algorithms on satellite imagery.