Objectives of the service
The REGREEN solution supports the transition towards climate-neutrality in the energy sector by providing insightful data analytics to locate the highest-potential sites for renewable energy production. In particular, REGREEN focuses on co-generation from three renewable energy sources (RES): solar, wind and hydropower. The purpose is overcoming the fundamental challenges to RES penetration, namely availability, reliability, and transmission stability. If a site has a high generation potential from multiple RES, a system can be created where the fluctuations or unavailability of one source can be compensated by the energy generated from another source, thus guaranteeing availability and stability without resorting to batteries. REGREEN combines Earth Observations and actual power generation data from existing RES plants as training features of a Machine-Learning model that forecasts the RES power production at any desired location on the map.
Users and their needs
The REGREEN solution targets the following customer segments, initially in the Italian market:
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Energy providers, who need to reduce their dependence on fossil fuels for energy production and increase their share of production from renewables. The REGREEN solution informs them about the most effective energy source to be exploited at existing fossil-fuel plants that need to be phased out and on new terrains to be used for renewable energy plants.
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Energy and Satellite service providers, who lack innovative products to offer to clients interested in renewable energy generation and with the REGREEN solution have the opportunity to provide additional intelligence for customers willing to enter the energy markets.
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Local authorities, that have the power to attribute land destination of use and issue energy production licenses by only relying on administrative constraints (distance from landmark, critical infrastructure, etc.). REGREEN can enhance their decision-making process with analytics about the energy generation potential of sites.
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Local energy communities, that would benefit from the insights gained through the REGREEN solution to decide on the most convenient and effective sources to exploit, especially in rural areas.
Service/ system concept
The REGREEN solution uses satellite data combined with regional renewables power output as input for a set of artificial-intelligence (AI) models. The input of the AI models are features such as daily radiation, temperature, wind speed, humidity, and terrain elevation, slope, and orientation. The target dataset on which the AI models are trained with supervised learning techniques is the local power output data of renewable energy sources (RES). As a result, the AI models learn to associate the input features at different geographical locations to the total power generated at that site from the different RES. This will be converted into a set of three energy generation potential indexes, one for each renewable energy source, the values of which the AI will be able to predict for any geographical location. The expected output of the project are high-resolution georeferenced maps of the energy generation potential indexes, that enable to visualise the sites with the highest potential for energy generation and co-generation from multiple RES.
Space Added Value
The project uses Earth Observations and ERA5 reanalysis data from the Copernicus Data Store, including weather-related variables, such as solar radiation, temperature, humidity, wind speed, and ground properties, such as elevation, slope, and orientation. While future versions of the REGREEN solution aim to incorporate direct Earth Observations from Sentinel missions, starting with ERA5 data offers advantages in terms of homogeneity, spatial and temporal resolution (approximately 2-10km and 1 hour, respectively, over Italy), and extensive coverage over several decades. Compared to ground monitoring, space observations enable a faster, more scalable and cost-effective solution to identify high-potential energy generation sites.
Current Status
The project activities have focused on identifying the target users of the REGREEN solution to better define the value proposition. We conducted three interviews with key stakeholders, namely an energy provider in the Italian market, the representatives of a public national authority that issues the necessary permits to use such land for the construction of renewable power plants, and the representatives of a photovoltaic-energy service provider and consultant for the building and installation of large-scale photovoltaic systems. These interviews identified the criteria influencing our respondents' decisions regarding the implementation of Renewable Energy Source projects and showed that, at present, these stakeholders lack information about the expected energy generation potential at the production sites. The technical development of the REGREEN solution focused on the conceptual dataflow for the AI model, and data exploration of the atmospheric dataset from COPERNICUS for each Italian province to identify and analyse relevant statistics.
