REPSCORE - Renewable Energy Performance Score

"To meet future energy supply and Paris Agreement targets, the Renewable Energy share must double by 2030, requiring annual average investment of over $900 billion, considerably more than currently achieved ($286 billion in 2015). Analysis of 2016 IRENA and BNEF reports and extensive market consultation with renewable energy project developers, investors, support networks and leading renewable energy sector experts, has identified that one of the main challenges constraining investment in the RE sector is the ability to accurately calculate the return on investment of projects and thus determine a project's value as an investment proposition using data in a cost effective, time efficient way. The lack of sophisticated, data-driven tools to determine the 'bankability' of projects cost-effectively and time-efficiently, leaves renewable energy market uncertainties unaddressed, resulting in a lack of investment and a loss of opportunity for renewable energy project developers and services providers throughout the value chain.

The proposed project seeks to develop a data-driven assessment tool, 'Renewable Energy Performance Score (REPSCORE)' which Enian Limited can use to more accurately, efficiently and cost effectively pre-qualify Renewable Energy projects for users (renewable energy investment and development teams operating globally) of their digital Deal Management and Collaboration Platform (DMCP). REPSCORE uses predictive algorithms to rapidly and accurately determine the economic and technological performance of projects to more efficiently and cost effectively pre-qualify projects on Enian's DMCP, radically enhancing decision-making for investors and accelerating the overall pace of capital deployment into renewable energy projects.

Enian in collaboration with mathematicians from the University of Edinburgh (UoE), have developed a basic MVP (TRL3) model. This project will assess the feasibility of transforming the MVP into an automated web-based, data driven application (TRL5) to assess both operational and non-operational projects, and the feasibility of integration with Enian's DMCP. The algorithmic based automated means of renewable energy project qualification will reduce costs of data analysis; accelerate project assessment process, and improve decision making by reducing and quantifying uncertainty as well as reducing human error and bias =\> boosting private investment in renewable energy projects by ~20%. The project will deliver significant export led growth for lead applicant Enian, a substantial ROI, increased employment and further opportunity for R&D investment. Project partner UoE will gain crucial commercial knowledge to be applied to future R&D."