Energy Saving Verification Service for increasing the trust on Energy Performance Contracts
VEOLIA in this pilot is covering the role of ESCO, aiming at generating analytics to improve the ESCO model. This business model is based on achieving a proper balance in between the investment and the energy savings achieved to reduce the ROI as much as possible. In the process of deciding if a project is economically feasible (viable) or not, the “data” are relevant, i.e. data from potential Energy Conservation Measures, cost, energy cost projections, as well as prediction of future energy behavior / performance. Regarding the Energy Performance Contracting and their compliance, the energy savings verification is a crucial task, so methods (based on protocols like IPMVP) to estimate savings need data: static and dynamic data to build simulations, estimations or statistics approach or real / live data from monitoring / metering networks / devices.
Enablers of the financing of refurbishment actions in the building stock at local level [GDYNIA] Motivation
A package of solutions will be provided to support the implementation of data-driven services for one-stop-shops. In this line, services to automatically generate models that support the reliable estimation of the energy status of buildings and calibrate the results based on real monitored energy consumption or through invoices will be provided. These will be complemented with the inclusion of the bankability evaluation of the refurbishment solutions, which will enable to link the refurbishment solutions proposed to the appropriate financing mechanisms.
Services to support reliable, cost-effective and better quality Energy Performance Contracts and Investments
LEIF is the only institution in Latvia that has reliable data on investments in EE and actual performance of investments in terms of energy savings. The pilot concept will demonstrate MATRYCS framework through cross-domain integration of a variety of heterogeneous historical and live data on financial performance, underlying EE impact of the investments, through historical extensive smart meters data integration. The aim is to collect and process data from smart meters, and apply machine learning algorithms, in order to better predict energy consumption and calculate and monitor the energy savings achieved. As a result, EPC and investments will be more reliable, cost-effective, and of better quality.