Periodic Reporting for period 1 - I-NERGY (Artificial Intelligence for Next Generation Energy)
Okres sprawozdawczy: 2021-01-01 do 2022-06-30
Artificial Intelligence (AI) is expected to radically transform the energy sector, redesign and shape the energy value chain and revolutionise the way in which Electric Power and Energy Systems (EPES) community is undertaking business processes. The use of AI is acknowledged to be of utmost importance for energy utilities to improve the performance of their business processes, and for power network operators to increase the stability of their network, within the renewable energy-based emerging decentralised paradigm.
At the same time, AI proliferation in the energy sector holds the premise for a larger environmental and social impact, by affecting environmental sustainability, strengthening social relationships among members of local communities and contributing to alleviate energy poverty. Energy fingerprinting might be leveraged to deliver different consumer-centred innovative AI-based services which bring a quite high social value to individuals and/or local communities. Hence, AI can contribute to finding solutions to some of the most pressing societal challenges, such as the fight against climate change, environmental degradation and the challenges linked to sustainability.
In this context, the constantly increasing momentum of AI, Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL) represents an unprecedented opportunity for the EPES community, aiming at increased grid flexibility, optimised maintenance and/or optimal operation, as well as delivering social-oriented consumer-centred services at the interplay of energy efficiency, energy management, personal comfort, green energy purchasing, cross-sector energy vs mobility, and energy fingerprinting based social services (e.g. personal safety / security and AAL care management).
However, scarcity of AI expertise in the energy community, fuzzy and unclear regulations on access to data, and uncertain business cases are hampering the full exploitation of AI along the energy value chain, preventing also the development of system level models and cross-stakeholder services.
The overall vision of I-NERGY is to deliver an energy-specific open modular framework for supporting AI-on-Demand in the energy sector (AI4 Energy), by capitalising on state-of-the-art AI, as well as IoT, semantics and data analytics technologies. I-NERGY will enable AI-based cross-sector multistakeholder analytic tools for integrated and optimised smart energy management, based on seamless data/information/knowledge exchange under respective sovereignty and regulatory principles. I-NERGY aims at evolving, scaling up and demonstrating innovative AI-as-a-Service (AIaaS) Energy Analytics Applications, which will significantly contribute to achieve a techno-economic optimal management of the EPES value chain, especially for SMEs and non-tech industries, while leveraging on and complementing the AI resources and tools made available by the AI4EU platform.
On top of the I-NERGY framework, the project defines AI energy analytics services and digital twins (grouped under the ‘Energy Commodities Networks’, ‘Distributed Energy Resources’ and ‘Energy Efficiency and Non-energy related Services’ categories) that will be developed and tested within 9 pilot hubs (including 15 use cases). Moreover, different business cases will be created, considering the interaction of different EPES stakeholders and software developers via the I-NERGY Open Calls that will select 25 Technology Transfer projects led by SMEs [under Financial Support to Third Parties (FSTP)]. I-NΕRGΥ’s vision is summarised in clear, measurable, realistic and achievable objectives, divided into 2 main categories:
(O1) Reinforce the service layer of the AI-on-demand-platform.
(O2) Reach out to new user domains and boost the use of the platform through use cases and small-scale experiments.
At the same time, AI proliferation in the energy sector holds the premise for a larger environmental and social impact, by affecting environmental sustainability, strengthening social relationships among members of local communities and contributing to alleviate energy poverty. Energy fingerprinting might be leveraged to deliver different consumer-centred innovative AI-based services which bring a quite high social value to individuals and/or local communities. Hence, AI can contribute to finding solutions to some of the most pressing societal challenges, such as the fight against climate change, environmental degradation and the challenges linked to sustainability.
In this context, the constantly increasing momentum of AI, Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL) represents an unprecedented opportunity for the EPES community, aiming at increased grid flexibility, optimised maintenance and/or optimal operation, as well as delivering social-oriented consumer-centred services at the interplay of energy efficiency, energy management, personal comfort, green energy purchasing, cross-sector energy vs mobility, and energy fingerprinting based social services (e.g. personal safety / security and AAL care management).
However, scarcity of AI expertise in the energy community, fuzzy and unclear regulations on access to data, and uncertain business cases are hampering the full exploitation of AI along the energy value chain, preventing also the development of system level models and cross-stakeholder services.
The overall vision of I-NERGY is to deliver an energy-specific open modular framework for supporting AI-on-Demand in the energy sector (AI4 Energy), by capitalising on state-of-the-art AI, as well as IoT, semantics and data analytics technologies. I-NERGY will enable AI-based cross-sector multistakeholder analytic tools for integrated and optimised smart energy management, based on seamless data/information/knowledge exchange under respective sovereignty and regulatory principles. I-NERGY aims at evolving, scaling up and demonstrating innovative AI-as-a-Service (AIaaS) Energy Analytics Applications, which will significantly contribute to achieve a techno-economic optimal management of the EPES value chain, especially for SMEs and non-tech industries, while leveraging on and complementing the AI resources and tools made available by the AI4EU platform.
On top of the I-NERGY framework, the project defines AI energy analytics services and digital twins (grouped under the ‘Energy Commodities Networks’, ‘Distributed Energy Resources’ and ‘Energy Efficiency and Non-energy related Services’ categories) that will be developed and tested within 9 pilot hubs (including 15 use cases). Moreover, different business cases will be created, considering the interaction of different EPES stakeholders and software developers via the I-NERGY Open Calls that will select 25 Technology Transfer projects led by SMEs [under Financial Support to Third Parties (FSTP)]. I-NΕRGΥ’s vision is summarised in clear, measurable, realistic and achievable objectives, divided into 2 main categories:
(O1) Reinforce the service layer of the AI-on-demand-platform.
(O2) Reach out to new user domains and boost the use of the platform through use cases and small-scale experiments.
During the 1st reporting period of the I-NERGY project, emphasis was put towards defining the stakeholders’ needs that set the groundwork for the creation of I-NERGY services, as well as ensuring that the I-NERGY ecosystem is in line with the existing regulatory frameworks and monitors the overall ecosystem that it operates within. In this regard, a set of 93 usage scenarios were elicited, I-NERGY services were described, and I-NERGY Framework for Trustworthy AI was designed. A preliminary version of I-NERGY AI Conceptual Architecture was further defined and the first versions of the I-NERGY Data services and AI trained models, as well as of the I-NERGY AI Energy Analytics services were developed. Moreover, a comprehensive pilot planning, operation and measurement / verification plan was designed to guide the pilots’ implementation during the current as well as the following reporting period. During the same period the 1st I-NERGY Open Call was successfully launched, leading accordingly to the kick-off of the 1st Technology Transfer Programme. Finally, a number of assets were onboarded on the AIoD platform, while the project received considerable attention via a multitude of dissemination and communication activities.
I-NERGY envisions becoming the product that will revolutionize the exploitation of AI analytics and data technologies in the activities and businesses related to energy, by providing a definitive framework for knowledge extraction, as well as fine-grained business intelligence services to interested EPES stakeholders. To enable and realise its vision, I-NERGY will develop, adapt, deliver and deploy a framework for building the next-generation AI-enabled energy analytics, which consists of: An I-NERGY – AI4EU Interconnection layer which leverages Open APIs, in order to synchronise and make available I-NERGY technology enablers to the AI4EU community. A data sharing backbone as fundamental backbone to enable data/models/resources interoperable sharing among different data hubs [Data Services Layers] - These layers include a number of distributed intelligent collaborative federated nodes, which may act as Data Providers and/or Data Consumers, and will be deployed over: (1) A variety of energy and non-energy data (weather, geographical) sets and/or legacy energy interoperable platforms from smart grid/buildings assets, technologies and components, hence representing sources of data owned/managed by DSO, TSO, Market Operators, ESCOs, aggregators stakeholders; (2) External datasets, such as other off-grid domain data, including weather forecast or geographical data sets, simulated data as made available from research labs, open datasets crawled over the Internet and/or linked energy datasets. A ML Models Layer which will encapsulate the ML/DL/RL models that will be created and provided to be used in different contexts and use cases. An AI Energy Analytics Applications [Application Layer] - An AI Energy Analytics Suite will be integrated to enable the design and creation of energy analytics applications.
I-NERGY impacts are anticipated in the directions of i. Enriching and optimising the AI on-demand platform service offer and reinforcing its sustainability. ii. Boosting the deployment of AI-based solutions and services, enabling a larger user community to reap the economic benefits of AI, especially SMEs and non-technology sectors.
I-NERGY impacts are anticipated in the directions of i. Enriching and optimising the AI on-demand platform service offer and reinforcing its sustainability. ii. Boosting the deployment of AI-based solutions and services, enabling a larger user community to reap the economic benefits of AI, especially SMEs and non-technology sectors.