TRUST-PV involved digital twin modeling, decision support systems, and O&M-friendly solutions specifically for PV plant integration and grid hosting capacity.
INACCESS NETWORKS S.A.
Greek SME delivering data analytics, digital twins, and ML tools for solar PV monitoring and real-time renewable energy management.
Their core work
INACCESS NETWORKS is a Greek technology SME specializing in data-driven software solutions for the renewable energy sector. Their work centers on real-time monitoring, predictive analytics, and decision support systems for solar PV plants — helping operators optimize performance, reduce maintenance costs, and integrate renewables into the grid more reliably. They combine IoT connectivity, machine learning, and big data infrastructure to turn raw energy data into actionable operational intelligence. In EU projects, they contribute as a technology partner bringing software expertise in energy data management and digital twin modeling.
What they specialise in
MORE (Management of Real-time Energy Data) focused on machine learning, big data pipelines, pattern extraction, and time series analysis for renewable energy data.
TRUST-PV keywords include digital twin and decision support systems, indicating software tooling for simulating and managing PV plant behavior.
TRUST-PV addressed grid integration and hosting capacity challenges as part of friendlier PV deployment across different market segments.
MORE applied machine learning, pattern extraction, and time series methods to renewable energy data streams, pointing to growing ML capability.
How they've shifted over time
Both H2020 projects started in 2020, so the keyword split does not reflect a years-long trajectory — rather, it reflects two parallel workstreams within the same period. The first project (TRUST-PV) was grounded in applied engineering concerns: digital twins, O&M optimization, grid integration, and sustainable hardware for PV plants. The second project (MORE) shifted toward pure data science: machine learning, big data, time series, and pattern extraction from energy datasets. Taken together, the arc suggests a company that entered H2020 with domain-specific energy tools and is simultaneously building deeper analytical and ML capabilities.
INACCESS NETWORKS appears to be moving from applied PV monitoring tools toward broader energy data analytics — a direction that positions them well for future projects in smart grids, flexibility markets, and AI-driven energy management.
How they like to work
INACCESS NETWORKS has participated exclusively as a consortium partner — never as a coordinator — across both projects. With 28 unique partners across 11 countries from just two projects, they work within mid-to-large consortia rather than small bilateral arrangements. This breadth of partners relative to a small project portfolio suggests they are valued as a specialist software contributor that diversified consortia bring in for their data and analytics expertise.
Despite only two projects, INACCESS NETWORKS has connected with 28 unique partners across 11 countries — an unusually wide network for such a small portfolio. Their reach is European, spanning multiple national ecosystems, though their home base is Greece.
What sets them apart
INACCESS NETWORKS occupies a specific niche as a software-focused SME that bridges energy domain knowledge and data science — rare among Greek companies in this space. Where many energy technology providers focus on hardware or standards, this firm brings software tooling: digital twins, ML pipelines, and real-time data infrastructure. For consortium builders, they offer the combination of energy sector fluency and ICT implementation capacity that is harder to find in a single partner.
Highlights from their portfolio
- TRUST-PVThe larger of the two projects (€404K), it tackles the full complexity of PV grid integration — combining digital twin modeling, sustainable hardware considerations, and O&M decision support across multiple market segments.
- MOREManagement of Real-time Energy Data shows INACCESS moving into pure data science territory — machine learning and big data on renewable energy streams — expanding beyond their PV monitoring roots.