If you are about to produce a “green” electron, what do you do with it? Do you sell it now? Do you store it in a battery and sell it later? Or do you make hydrogen? And if you do, do you then convert it to ammonia?

What is the best course of action to maximise ROI for this electron that will vanish in a nanosecond?

You may not have the answer to these questions, or the ability to respond in the window of opportunity.

But could your computer?

A digital triplet may just hold the answer. The concept of the digital twin is well established at this point, offering a digital representation of a physical object or ecosystem.

Digital triplets add a layer of GenAI agents to this model. The move allows for interactive feedback and access to comprehensive analysis across multiple data sets and information sources for the end user.

The appeal of a GenAI agent is that users can interact with it using natural language and it can ingest multiple media types. These include images, text, documents and discrete data without extensive training or modelling.

A number of industries can use digital triplets, including planning, healthcare and manufacturing. But in the energy sector, this agentic tool can analyse operational digital twins, IoT data and business KPI models to advise energy companies, utilities and energy retailers on the best course of action.

A digital triplet, working for a utility, will ingest current power generation data from edge devices, align power sources and recommend grid management strategies. It will base these on environmental changes, providing real-time feedback to the operator.

Diagram showing the operations of a digital triplet

Digging into the capabilities

Digital triplets embody agentic AI, with the ability to trigger multi-purpose and context-based agents. They act autonomously based on a user’s request. These actions include deciding on data sources, considering information quality and detecting patterns in disparate data. They also interact with their environment and multiple sources in real-time.

A traditional AI or GenAI model responds to queries from a user. But agentic AI, within a digital triplet, continuously updates and refines its insights through ongoing interactions. These occur between the physical entity, its digital representation and the operational context.

This feedback loop allows digital triplets to take the initiative in adapting and responding. They can provide real-time advice and insights that reflect the dynamic nature of the systems they model.

There is of course a need for AI to act responsibly and ethically. Integrating the tool within the digital triplet ensures transparency, reliability, security, privacy and accountability are part of the the solution, with a human in the loop to monitor.

Safety in focus

As digital triplets operate in real-time and continuously update their models through interactions, they provide a clear and traceable record of decision-making processes. This ongoing feedback loop ensures that all actions taken by the AI, from data source to pattern detection through to a response, can be monitored, audited and evaluated.

This functionality helps with continuous improvement of the models and functionality and also assesses the quality of the data and information over time, supporting improved data governance processes.

Digital triplets can go further. By incorporating diverse data sources and providing comprehensive insights, digital triplets facilitate inclusive and equitable decision-making processes. In this way, they ensure applications are designed and implemented in ways that prioritise societal well-being and sustainability.

By simulating outcomes before an event occurs, a digital triplet can assess potential ethical implications before implementing decisions or policies. This enables the development of AI solutions that are innovative, aligned with human rights and values, and that support the detection of any unintended bias, potential misuse or societal harm.

Related Content

Tees Valley aims to transform industrial foundations into energy future

Right pieces, wrong picture for Scotland’s energy puzzle

Edinburgh engineers teach robots to ‘surf’ North Sea waves