4 Top Digital Twins Startups Impacting The Energy Industry

4 Top Digital Twin Startups Impacting The Energy Industry

We analyzed 54 Digital Twin Startups. Visualiz, Pratiti Technologies, QiO Technologies, and PETRA Data Science develop 4 top solutions to watch out for. Learn more in our Global Startup Heat Map!

Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on solutions for the energy industry. As there is a large number of startups working on a wide variety of solutions, we decided to share our insights with you. This time, we are taking a look at 4 promising Digital Twin Solutions.

Heat Map: 4 Top Digital Twin Startups

For our 4 top picks, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 4 interesting examples out of 54 relevant solutions. Depending on your specific needs, your top picks might look entirely different.



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Which startups develop the other 50 solutions?


Visualiz – Virtual Reality (VR) Enabled Team Collaboration

VR enabled digital twin technology enables designers, experts, and distributed operational teams to collaborate on potential modifications or issues based on 3D representations and using real-time data of existing systems. For example, the stakeholders working on overheating shafts of wind turbine motors can do so without being physically present at a location, allowing them to save costs on travel while wasting no time in making decisions. Norwegian startup Visualiz develops a platform for the visualization of physical assets in different industries, including in energy. They take advantage of VR to create a collaborative space for users where they can dive into the various aspects of energy assets, monitor their status online and detect areas for troubleshooting or improvement. Their VR space is customizable depending on the scope of a project.

Pratiti Technologies – Performance Analytics For Renewable Generation

While trying to forecast the precise power generated from renewable sources of energy like wind or solar, performance analytics of the project makes it easier to understand the life of an energy asset. Real-time updates on external weather conditions and failure alerts in the system, generated digitally, help in making decisions on whether the preset benchmarks can be reached or if adjustments are required. Indian startup Pratiti Technologies provides digital twin data analytics systems for solar energy assets. Their solution integrates Supervisory Control and Data Acquisition (SCADA) and remote monitoring systems to deliver features such as benchmarking, root cause analysis for failures, intra-day power prediction, and performance forecasting.

QiO Technologies – Predictive Asset Maintenance

With self-learning digital twin technology, it is possible to use predictive analytics in order to forecast energy asset failure, malfunction or damage. This allows for optimizing the maintenance process, moving from a reactive to a predictive maintenance model, in order to decrease unplanned and costly downtimes of energy assets. British startup QiO Technologies develop an Artificial Intelligence-infused maintenance application, for the oil & gas industry, to predict potential failures and recommend the best course of action for teams to prevent or mitigate them while minimizing downtime. The software has the ability to create dynamic diagnostics, especially when root causes are difficult to identify, to ensure the future operations do not face the same issues.

PETRA Data Science – Energy Systems Simulation

The simulation of energy systems aims to create a model-based representation of the technical processes occurring within a plant or system and identify their bottlenecks. Changing the parameters in a virtual energy system helps simulate the numerous real-world scenarios while allowing for informed decision making. Australian startup PETRA Data Science develops digital twins for the mining value chain for the oil & gas industry so as to integrate 3D geological, blasting, and fragmentation data with time series processing data. Machine learning algorithms allow for simulating the planning of the mining process and the process control options while predicting the overall plant performance.

What About The Other 50 Solutions?

While we believe data is key to creating insights it can be easy to be overwhelmed by it. Our ambition is to create a comprehensive overview and provide actionable innovation intelligence for your Proof of Concept (PoC), partnership, or investment targets. The 4 startups showcased above are promising examples out of 54 we analyzed for this article. To identify the most relevant solutions based on your specific criteria and collaboration strategy, get in touch.


Ready to discover your top Digital Twin Solutions?
Ready to discover your top Digital Twin Solutions?

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