Solar Capacity
twinCity Solar is a region-wide digital twin, enabling end users to optimise solar panel placement over arbitrary geographical areas for optimal power generation.
Transforming Renewable Energy with ML
By leveraging Bayesian Uncertainty Quantification, our solution engineers can help you augment your system design, enhance your asset placement, implement adaptive monitoring, and increase transmission efficiency throughout your network.
We are transitioning towards a new, low-carbon energy paradigm, motivated by a desire to improve efficiency, stability, and security. digiLab are experts in enabling organisations to apply Machine Learning to their systems and data, in order to both reduce costs and achieve sustainability goals.
twinLab is our cloud platform for applying probabilistic Machine Learning to your sensor data or asset simulations. Built-in Uncertainty Quantification makes it especially useful for making predictive decisions based on limited, noisy, or sparse data.
Powering Renewable Energy Solutions with twinLab
We are moving from a system that has been determined by commodity assets to a system that requires digital skills, new thinking and new technologies.
Get in touch to explore our technical demos:
twinCity Solar is a region-wide digital twin, enabling end users to optimise solar panel placement over arbitrary geographical areas for optimal power generation.
Incorporate building-to-city level layers of data to make predictive forecasts and optimally manage energy usage over large sites.
SenSiteUQ places sensors optimally across a network and delivers improved data quality using the right number of sensors.
Leverage the powerful uncertainty quantification capabilities of twinLab to unlock truly predictive maintenance and fleet health understanding.
How twinLab can Enable your Energy Transition