Renewable energy sources have some fluctuation due to weather and time of day. Fossil fuels have provided dependable,  energy for an extended period of time. Power grids have evolved to depend on high capacity, rapidly deployable power sources. The legacy of this is a centrally managed grid with limited flexibility, and control systems which are unable to optimize the use of renewables over fossil fuels.

Our software makes dynamic usage of renewable energy sources in smart grids possible. We have developed innovative technology leveraging new large scale sources of Internet of Things (IOT) data throughout the grid to enable optimal decisions to be made with respect to grid operations.  

Our AI solutions are deployed on accelerated hardware (GPU/FPGA) at the edge of the utility network, and in the cloud (public or private). The decisions our software makes enables the grid to achieve energy savings by optimizing the usage of renewable energy in conjunction with new storage and distribution technologies.  

We mine,  analyze and derive the optimal actions in real time, and near real time.  The resulting increased use of renewable energy in an optimized smart grid lowers the cost of energy by displacing fossil fuels, and reduces Greenhouse Gas (GhG) emissions associated with non-renewable power sources.

Artificial Intelligence

For Clean Energy


BluWave Inc. Artificial Intelligence

[email protected]

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Integration of Dynamic, Renewable Energy in a Smart Grid using AI‍‍‍


Each MW of electricity from renewable sources in place of fossil fuels will result in savings of $500K per year. This is equivalent to not burning 2700 metric tons of coal.

The new “energy internet” features the dynamic flow of data and energy in many directions within a smart grid. The complexity created by intermittent renewables, storage, local generation and changing transportation technology means grid control must be smarter, faster and more adaptive. Utilities need to:

Current grid management and control processes are not able to deliver optimized performance when managing new renewables, storage, distribution and usage models in real time. Our AI based solution develops the best models for optimizing a dynamic grid. We change the rules and associated decisions as the data changes, saving energy costs by leveraging renewable sources and storage solutions in the new modern smart grid.

What We Do and What We Save


Maximizing Renewable  energy usage for grids in real time using AI inference and training.

Using grid sensor, smart meter‍‍‍,  pricing, weather and  IOT data for rapid energy optimization.

‍‍‍Artificial Intelligence For Renewable Energy ‍‍‍Smart Grids







At BluWave-ai our team wakes up every day energized to assist in the transformation of the world to renewable energy and managing it with better AI driven computing.

Renewable energy sources are a variable source of power, and are  affected by weather. BluWave-ai's software and systems enhance ‍‍‍ renewable energy usage in smart grids for electric utilities. Due to weather fluctuations,  wind based power production is unavailable at low wind speeds, and solar performance is reduced by 70% in heavy cloud cover.

Our vision is to deploy technology that optimizes the use of energy by applying Artificial Intelligence (AI) to the problem of optimizing the performance of an energy grid with renewable, storage and other sources of power.

Who Benefits?

  • Net zero communities trying to go to renewable sources for their operations
  • Large enterprises in manufacturing trying to reduce energy costs and GHG. A large auto manufacturing operation can spend $40M on electricity for the production of 200,000 cars
  • Hospitals, University campuses aiming for a greener future
  • Large Mining and forestry remote operations in areas served by diesel resupply only for their electricity
  • Remote northern  communities and underserved off grid communities in the developing world to help get them to energy self sufficiency
  • Utility Scale transmission, generation and distribution optimization
  • Optimize demand and supply side energy management
  • Coordinate distributed and local energy storage
  • Integrate micro weather trends into power generation and consumption in real time
  • Maximize electric vehicle charging and localized dynamic storage during low loads
  • Dynamically integrate local renewable energy
  • Enable and disable IOT nodes
  • Reduce costs by generating energy optimally from local sources