Balancing the Power Grid with Machine Learning

Our society is undergoing rapid electrification, with increasing electricity consumption for heating and transportation, as well as a rise in renewable energy generation. However, it is crucial to maintain a balance between production and consumption. With the growing dependence on intermittent renewable sources like wind and solar power, the power grid experiences stronger fluctuations, making it increasingly challenging to regulate demand through pricing alone. While "hardware" solutions like batteries and a robust power grid can help offset these effects, they come with high costs and scalability issues.

Managed Grid presents a complementary approach to navigate this energy transition. By harnessing advanced machine learning models, we can forecast the imbalance between electricity consumption and production for up to 48 hours in advance. These predictions enable us to effectively manage the peaks of imbalance by leveraging surplus energy to charge batteries and discharging them during shortages. Additionally, the forecasts provide valuable insights to energy-intensive consumers and producers, guiding them on the optimal and most profitable times to consume or produce energy.