This document is a deliverable of the SEDIMARK project, funded by the European Commission under its Horizon Europe Framework Programme. This document presents the “D6.2 Dissemination and exploitation plan” deliverable, including the expected impact of the ongoing and planned activities, target audience, milestones, and mechanisms to assess the dissemination and exploitation activities carried out throughout the project execution.
Dissemination activities are any action related with the public disclosure of the project results by any appropriate means, including scientific publications. On the other hand, Communication activities also include the promotion of the project itself to multiple audiences, including both the media and the public. Separating the concept and the goal of dissemination and communication plan is important as the communication plan is about the project and its results, whilst the dissemination one is only about the results.
Moreover, exploitation activities have a broader scope compared to communication and dissemination. They can include actions such as utilizing the project results in further research activities other than those covered by the concerned project, developing, creating and marketing a product or process, creating and providing a service, or even in standardisation activities.
Machine Learning introduction
Machine Learning (ML) is a modern and efficient branch of AI (Artificial Intelligence), specialised in pattern recognition within data streams. It can provide precise analysis based on statistics to detect insights from a large data set, using the same principle as human neural networks in our brain. Every system equipped with ML must learn and discover patterns from historical data and compare its predictions with real data, before providing reliable information. That is why AI systems are trained with as much data as possible.
ML algorithms are more efficient than traditional modelling methods and can surpass human intelligence through its powerful computational efficiency. For instance, image recognition and time series analysis are well-known and widespread domains of application of ML for real world cases, such as the EU-funded SEDIMARK project. SEDIMARK aims at building a secure, trusted and intelligent decentralised data and services marketplace over several years, using ML in order to automate data quality management. Over time, the project results will provide ever-increasing accuracy and precision with its growing data sources.
ML could be directly used on edge systems to ensure data quality. Some algorithms are specialised for this purpose, with low power consumption and modest memory size. For instance, EdgeML and TinyML are open-source libraries that provide this kind of outcome.
ML embarked on edge systems
The IoT platform from EGM (i.e. the EdgeSpot) is compatible with both libraries and could manage and distribute FAIR data in an energy efficient way. The ONNX (Open Neural Network Exchange), an open format representing ML models, may be a solution to select the right combinations of tools. And finally, with the help of the use cases provided within SEDIMARK, the project might elaborate a concrete strategy to automate and manage data quality.
SEDIMARK plans to build a distributed registry of resources stored on edge systems, close to where data is generated. The purpose is to clean, label, validate and anonymise data.
A digital twin is at its highest level, an architectural construct that is enabled by a combination of technology streams such as IoT (Internet of Things), Cloud Computing, Edge Computing, Fog Computing, Artificial Intelligence, Robotics, Machine Learning and Big Data. Analytics.
The digital twin concept is based on the fact that every physical part has a virtual part that is conceptually, structurally and functionally the same as the physical part. The concept of Digital Twins dates to the 1970s, used by NASA in the Apollo 13 mission. Nowadays Digital Twins are used in various industries, being a key concept in realizing the communication mechanism between the physical and virtual worlds using data.
The primary use case for Digital Twin is asset performance, utilization, and optimization. Digital Twin enables monitoring, diagnostic and forecasting capabilities for a specific use case.
Examples of Digital Twin application scenarios are described in the following:
- Digital Twin for creating 3D modelling of digital objects from physical objects. This use case is a critical success factor for smart manufacturing initiatives.
- Digital Twin is used in factories to identify symptoms with constant monitoring and find the root causes of production problems.
- In healthcare, Digital Twin is used for simulation purposes so that doctors can perform risky operations first in a simulated environment before performing the operation on a real patient.
- Urban planners use Digital Twin initiatives using virtual models to improve city conditions in a proactive manner. This approach can reduce complexity and simplify processes for planners.
Digital Twins and Data Spaces
Digital twins must be considered in their relationship to data spaces. A larger overview is therefore required, including systemic oversight, and supporting infrastructure.
International Data Spaces (IDS) provides data space technologies and concepts for various application domains that enable standardized data exchange and integration in a trusted environment. The International Data Spaces Association (IDSA) is a non-profit organization that promotes IDS architecture as an international standard in a variety of fields, including healthcare, mobility, agriculture, and more.
It is expected that in the medium term, in strong relation to specific requirements, collaboration solutions with centralized data storage in one or more clouds and distributed data storage with efficient data processing will be realized by combining the Digital Twins with Dataspaces.
With help of the use-cases provided within SEDIMARK, the project could elaborate a concrete strategy in which this relationship between Digital Twins and Dataspaces can prove of real value.
Digital solutions are important to the energy industry because they can help the energy system become more flexible, reliable, and efficient while also making it possible to incorporate renewable energy sources.
Because they can make it possible to share and analyse large amounts of data from a variety of sources, such as data on energy production and consumption from smart meters, weather data, and information about the grid, Data Marketplaces may be especially useful for the energy industry. Energy storage, grid management, and the integration of renewable energy sources can all benefit from a better understanding and management of the energy system by energy companies and grid operators. New business models like peer-to-peer energy trading and the integration of electric vehicles into the energy system can also be made possible by Data Spaces.
By unlocking the value of data and ensuring that data can be shared and reused across various industries, the European Commission wants to create a society and economy driven by data, according to the European Strategy for Data. Additionally, the strategy emphasizes the significance of data in achieving the EU's energy and climate goals.
The Green Deal is a plan by the European Commission to make the EU's economy sustainable by turning climate and environmental problems into opportunities in all sectors and making the transition fair and inclusive for everyone. It includes smart grid and metering systems, digital platforms for sharing and analysing energy data, and integrating distributed energy resources like electric vehicles and small-scale renewable energy production.
However, the question of how to apply these ideas to the current energy landscape is still unanswered. For instance, how can we guarantee that frameworks for data governance and management are in place to facilitate data access while safeguarding privacy and security? In terms of the integration of renewable energy sources and the creation of novel business models, how can we encourage the development and implementation of digital technologies and data solutions in the energy sector? And how can we make sure that the benefits of digitalization are available to everyone, especially in terms of reducing energy poverty and ensuring access to energy? To fully realize the potential of digital technologies and data for the energy sector, these are some of the crucial questions that must be answered and SEDIMARK will try to find the way to contribute to solve at least some of them. Get aboard the SEDIMARK cruise and share with us the experience!
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