Data analytics for Improving the Financeability of En- ergy Efficiency Investments

Energy efficiency (EE) projects are often fragmented, of high transaction costs, and fall below the minimum value that many private financial institutions are willing to consider. The finance community is lacking a tested, evidence-based platform, providing decision makers with support regarding the impacts of various investment criteria, risk aware assessment, and performance applied on a pool of EE investments. The capability offered by emerging near big data analytics to integrate cross-domain financial and energy consumption is key to building the necessary market confidence in EE projects and making them an attractive investment asset class. The availability of comparable, anonymized historical data pooled from major market segments, structured along major project characteristics, can encourage greater EE investment flow. The aim is to present data-driven applications based on machine learning methods that can attract and mobilize private funding on such projects, providing investors/financiers (e.g., commercial/green investment banks, institutional/insurance funds, etc.) and project developers (public/local authorities, energy providers, ESCOs, construction companies, etc.) with data and tools to identify sustainable investment pathways and decrease the EE investment risk. Extensive data processing is applied to elaborate and categorize financing instruments and risk mitigation strategies, and to identify best practices on private financing as a basis for benchmarking.