Artificial intelligence has the potential to change how utilities address a wide range of challenges, including how they help customers stay cool during the dog days of summer.
Consider how utilities currently address this very predictable dilemma.
When family members return home at the end of the day, utilities experience large demand spikes as air conditioners turn on and draw large amounts of power. If the utility isn’t producing and transmitting enough power to meet demand, brownouts and quality issues can result.
Traditionally, utilities have addressed this challenge through peak-shaving. That is, they have fired up fossil-fueled generators to provide the additional power needed to meet the increased demand.
But a combination of emerging technologies, including predictive weather analytics and battery energy storage, could provide a cleaner and smarter solution to managing energy demand. Predicting the weather also empowers utilities to increase their use of solar and other renewable sources that have been constrained historically by their unpredictability.
How Might It Work?
As the sun beats down, complex machine-learning algorithms can be used to monitor the photovoltaic output of local solar energy production sites throughout the day. These algorithms determine if excess energy is being produced and, if so, whether it could be stored or rerouted to an area where cloud cover is limiting another solar site’s energy production.
Most excess solar energy is produced during midday — typically between noon and 2 p.m. This energy in particular could be stored, routed and deployed to locations that the algorithm determines are most taxed at the end of the day, when families wish to do their laundry, turn on their electronics and relax in the cool.
It Gets Better
As the quantity and quality of weather data increases, and as heat waves can be predicted by pattern recognition algorithms, your utility can plan the allocation of resources proactively. While it is common sense that summer temperatures are going to be high, artificial intelligence can provide early warnings when variations in temperature and humidity will likely affect customer comfort and air conditioning use.
By knowing as many as three days in advance when large spikes are expected, the utility can develop strategies for optimizing energy storage and drawing on battery resources to prevent quality issues. The more robust information utilities can collect in real time and process with historical models, the better they can create and bolster plans that keep customers comfortable.
Artificial intelligence-driven technologies like this will continue to advance. As they do, utilities will be presented with unique opportunities to use them to optimize resources. The analytical and predictive capabilities made possible by these technologies will help both with planning and decision-making. Artificial intelligence has the potential to save utilities and customers alike time and money, as well as to reduce peak demand and improve the ability to deliver seamless service to users.
Keeping customers cool during extreme temperatures represents just one of many emerging utility applications for artificial intelligence. Learn about some of the other exciting ways it is expected to change the way utilities are operated and maintained in our recent white paper.