Mike Hejmej
CEO, Co-founder
As the power grid grows more complex and electricity use continues to rise, utilities are under more pressure than ever to keep everything running smoothly. One of the most significant shifts happening behind the scenes is the use of artificial intelligence to sharpen load forecasting and modernize demand response. What used to be a slow, reactive process is quickly turning into a real-time, data-driven operation that helps utilities balance supply and demand with far greater precision.
New era of load forecasting
For decades, utilities leveraged fairly simple models built around past usage data and basic weather forecasts. Those tools worked well enough when energy flows were predictable, and the grid was largely one-directional. Today, the landscape looks nothing like that. Modern forecasting tools pull data from a long list of sources. Real-time consumption feeds, detailed meteorological data, local event calendars, economic signals, and even population trends. Instead of treating each factor separately, AI blends them to spot connections and patterns that older models would miss entirely.
The rise of distributed energy resources has made this even more critical. Rooftop solar, home batteries, smart appliances, and electric vehicles have turned customers into active participants in the grid rather than just consumers. With energy now flowing both ways, forecasting has become more complex than ever, and AI is proving to be the only practical way to make sense of it all.
Why AI excels at prediction
AI-driven systems don’t just collect more data; they learn from it like humans. By reviewing historical usage and tracking real-time behaviour, these models can lock in on patterns that are too subtle or too dynamic for human analysts to catch. They continuously adjust as conditions change, refining their predictions each time they run.
One of the most significant advantages of AI in this space is its ability to connect dots that seem unrelated. A traditional model might treat weather, sporting events, and industrial activity separately.
An AI model, on the other hand, can recognize that a heatwave during a significant event weekend creates a particular type of load spike in a specific region and help plan for it. This kind of predictive depth helps utilities anticipate stress points on the grid before they occur, giving operators time to take action rather than scrambling in response.
Demand response gets a serious upgrade
Better forecasting unlocks more thoughtful demand response, and utilities are taking full advantage. Instead of relying on broad, time-of-day programs, utilities can now make targeted, real-time adjustments to smooth out demand spikes. This reduces strain on the grid and reduces the need for costly peaker plants that typically fire up during high-demand periods.
More accurate predictions have also led the way for new pricing models. Dynamic rates that shift throughout the day based on grid conditions are becoming more common. Customers can get a clearer sense of when energy is most expensive, while utilities get a more responsive and flexible load curve, which is much better for the grid.
A win for customers
Customers feel the impact, too. More accurate forecasting leads to fewer outages and more reliable service. On top of that, advanced demand response programs give households and businesses more ways to manage their energy use and often help them save money. As a result, many customers have also started participating in initiatives that support community resilience and sustainability.
Whats next?
The level of granularity AI provides will open the door to hyper-responsive load management and support the continued rollout of smart grid technologies. Renewables add both complexity and opportunity. Solar and wind generation fluctuate in ways that challenge traditional forecasting models, but AI is particularly good at managing variability. As utilities expand their renewable portfolios, AI will be essential for keeping the grid stable and reliable.
AI-powered forecasting and demand response are no longer experimental. They’re quickly becoming core tools for modern grid management. By adopting them, utilities can better navigate rising demand, support sustainability goals, and deliver more reliable service. As electrification accelerates, the role of AI will only grow, helping utilities manage an increasingly dynamic grid while maintaining the reliability that customers depend on.
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