Our primary focus is on deriving and delivering data-driven insights for the energy industry that is undergoing massive decarbonisation and decentralisation. Dtime understands how data analytics and machine learning could provide continuous value to different players such as demand response aggregators, battery storage providers, electric vehicle fleet managers, renewable generators, energy suppliers, system operators and new energy businesses.
Demand response aggregators
play an increasingly important role in the energy system by offering flexibility. Accuracy of flexibility forecasts will influence their revenue gains as well as impact the system reliability. Advances made in machine learning have demonstrated the potential for accurate flexibility forecasting.
Battery storage providers
are delivering grid balancing and ancillary services in different electricity markets. Data-driven models for simulation, optimisation and prediction have the capability to inform investment and operational decisions.
have the responsibility to procure energy for their consumers from the generators. Reliable energy generation/demand forecast models are necessary to help the suppliers plan ahead of time.
Renewable energy generators
depend on the availability of natural resources such as solar radiation and wind for energy generation. Accurate generation forecasts from solar and wind farms derived with the help of state-of-the-art machine learning models can improve the energy system reliability to a great extent.
maintain the reliability of the energy system. Being able to anticipate outages and congestion using accurate predictive models help them perform this task efficiently.