Genius Energy Manager – Edge: Development and Validation of an innovative integrated solution for Distributed Retail Stores Application
GEM-Edge designs and implements a low-cost fog computing node to be installed in retail shops or other distributed sites, to run AI models and advanced analytics at the edge. This fog node enables effective and responsive processing, filtering and elaboration of data related to energetic, environmental and business KPIs, offering a new solution for:
- real-time optimisation and actuation of relevant parameters for energy consumptions,
- air quality monitoring,
- control of business relevant parameters.
GEM-Edge goes beyond the limitations of pure cloud-based approaches to energy and environmental analysis, which, in the cases of large and geographically distributed deployments of IoT sensors and devices, require massive (and potentially costly) data transfers from the edge to the cloud, introducing latencies that delay real-time actuation.
|Energenius||Technology provider||Energenius is an innovative start-up with high level know-how and technological content born from the synergy between energy managers and engineers who are experts in measuring and analyzing energy measures.|
|Hub Innovazione Trentino||DIH||The Digital Innovation Hub of Trentino offers technology transfer services for businesses and in line with the indications of the Industry 4.0 Plan of the Italian government, favors the digitization and introduction into production processes of the most relevant information technologies.|
The output of GEM-Edge project is the development of an edge node including both hardware and software components. Such element will be included in an integrated commercial product (GEM-Retail) to be installed in the main area of small and medium distributed retail shops. In general, GEM-Edge is be suitable for all the applications of geographically distributed small and medium buildings and spaces for tertiary use related to a unique owner or manager (e.g. bank agencies or different spaces managed by a Public Body).
Almost everywhere, air composition is unknown. Consequently, it is impossible to detect if the air composition of an area is potentially harmful to individuals’ health or contributes to climate change. Human activities (e.g. housing, transport, industry) are an important source of air pollutant emissions (NOx, SOx, PM, etc.), affecting the well-being/health of people, and greenhouse gases (CO2, CH4, N2O, etc.), affecting the environment people are living in.
GEM-Edge develops a fog computing hardware with AI-based software to deal with data coming from field devices, to serve retail customers with a specific focus on energy and environmental parameters, in order to improve consumer welfare, energy efficiency and business processes.
The GEM-Edge platform provides an innovative solution to achieve the following goals:
- data monitoring: real-time data from smart meters installed in the shops, to gather:
- energy consumed by lighting and by heating, ventilation, and air conditioning (HVAC)
- temperature, humidity, saturation, CO2 levels, also taking into account the new regulations after the COVID-19 global pandemic
- customers’ intensity flow and other business KPIs defined by the specific customer
- real-time energy optimisation, according to point 1
- actuation of specific configuration sets (HVAC system shutdown after closure, consumption check after hours, lights turning off if needed, …)
The final outcome of the AE will be a prototype consisting of a selected low-cost, commercial off-the-shelf hardware node, over which Energenius will run a micro-services-based software able to:
- collect energy, air quality and business data from IoT devices,
- apply novel data filtering algorithms, tailored to the energy management domain
- run AI models trained and validated in the cloud
Among our customers, we have selected Calzedonia Group SpA, to be our pilot in this AE. The AE is also expecting to install the GEM-Edge nodes with sensors and gateways in at least 3 pilot shops owned by Calzedonia in Italy. The metrics to evaluate the success of the AE will be both technical (e.g. stability and scalability of the system, accuracy of the models) and economical (economic advantage, e.g. energy cost reductions).
Positive results from this AE will open the chance to enter this new market segment, with a potential 40% improvement of company global revenues.