Predictive Maintenance - HELIOS-AI
Predictive maintenance for a public lighting control system
Predictive maintenance for a public lighting control system
A cancer support chatbot driven by scientific evidence and ethical AI, leading to deep data & metadata collection for R&D & patient empathy
Enabling the local agency to plan the deployment of renewable sources in an effective manner, considering the future changing conditions derived from climate change.
Contributing to EPC being more reliable, user-friendly, cost-effective, better quality, and EU legislation-compliant.
Reducing the uncertainty linked to energy efficiency investments, which can be attributed to the lack of relevant skills and ability to assess investments.
Conceptualising the non-energy services for personal safety/security and AAL based on the deployed sensor infrastructure and aggregated data from elderly care house.
Investigating opportunities for prosumers as providers of balancing and ancillary services in the energy market.
Better prediction of impeding changes in retail client load distribution due to the increasing numbers of EVs in Greece. Prediction of availability of publicly available charging stations.
Improvement in operational efficiency of PV assets through the combined effect of optimised maintenance and increased assets efficiency. Increased self-consumption from local RES and electricity cost reduction.
Improvement of the asset management and the operational efficiency of the local smart distribution grid; deployment of cross-network coordinated management and operation with water networks.
Local power network congestion management is highly required due to high shares of intermittent RESs in the ASM headquarters area, as well as the need for reducing the reverse power flow power flow.
The aim is to support a condition-based and risk-based maintenance of both existing and innovative power components, as well as for the new digital environment.