The intermittent nature of renewable energy sources like wind and solar creates challenges for maintaining a stable and efficient electrical grid. Fluctuations in energy supply and demand require advanced energy storage solutions to ensure a consistent flow of clean energy. Current algorithms for managing energy storage systems often lack the adaptability needed to maximize the usage of clean energy and reduce carbon emissions.

Keeling Labs develops self-optimizing energy storage systems using machine learning algorithms to enhance the efficiency of clean energy usage in the electrical grid. Their technology addresses the variability of renewable energy sources by creating models that learn how a battery should optimally charge or discharge. This approach maximizes the capacity for clean energy storage, enabling the grid to run on more clean energy and directly reducing carbon emissions. By reinventing these algorithms, Keeling Labs unlocks gigawatts of untapped energy storage capacity.

Reinforcement learning models that continuously adapt to changing grid conditions

Algorithms that optimize battery charging and discharging to maximize clean energy usage

Real-world deployment of models for standalone energy trading

Technology that directly controls how clean energy is used to displace carbon emissions

Keeling Labs primarily targets grid operators, energy providers, and organizations focused on integrating renewable energy sources into the electrical grid.