Utilities Tech Outlook Weekly Brief
Be first to read the latest tech news, Industry Leader's Insights, and CIO interviews of medium and large enterprises exclusively from Utilities Tech Outlook
AI can help with on- and off-grid energy management, starting with monitoring renewable supply reliability.
FREMONT, CA: Many countries are transitioning from the old model of a net energy supplier to one that includes clean and green power and a bi-directional flow from the user to the grid. Providing reliable power and controlling a consistent baseload is a complex process that involves SCADA, HMI, and IoT devices. Smart meters, the potential of a prosumer to send energy back to the grid from his electric vehicle or solar rooftop, and intermittent energy sources like solar and wind make the equation too complex for human monitoring.
Energy storage disrupts new energy paradigms. There are several types of energy storage, from pumped storage and molten salt for massive on-grid solutions to lithium-ion batteries for mobile devices and municipal utility backups. AI and machine learning will improve energy efficiency and scale, but it's not perfect. For better forecasting and decision-making, combining more data must be essential. There is less trust in AI forecasts until there is a significant base of non-skewed data and machine learning, so judgments will still need human scrutiny. As software evolves and data becomes more dependable, AI applications may be relied on more to make optimal energy decisions.
AI can increase energy storage in numerous ways.
Grid safety - When a fault is discovered, both lines are live. AI can detect defects and deliver proactive warning messages to technical support and consumers. AI can monitor battery life and malfunctions and transmit the information to support. Industrial and residential peak demand used to be time-bound, like when homeowners turned on their cookers to make dinner. Peak demand, which traditionally occurs during the day, is migrating to night when solar is not accessible. AI can interact with a resident's smart meter to reduce domestic geysers and balance the load.
Optimizing energy storage and release - AI can detect when surplus energy may be stored or needed to meet peak demand. Efficient supply and demand control utilizing battery storage minimizes carbon-positive demand management therapies like diesel. AI can manage supply and demand by analyzing smart meter data to predict real-time demand peaks and troughs. It uses AI to become a leader in energy forecasting.
