Artificial Intelligence (AI) is revolutionizing the way we manage the grid, as AI allows for more precise load forecasting that will boost overall reliability. Firms such as Hydro-Québec are at the forefront of technological innovation. They put AI to use predicting future electricity demand and taking probative action to avert future blackouts. By integrating AI into their systems, utilities are poised to achieve more accurate forecasting and proactive management of energy resources.
Hydro-Québec used AI for short-term forecasting, enabling them to forecast electricity loads in 36 hours or less. This capability is further enhanced by AI-powered, hourly forecasts out to 10-12 days in advance, based on unique, proprietary insights from meteorologists. AI can provide longer-term outlooks based on historical weather patterns, ensuring that energy companies can adapt to changing conditions effectively.
The utility company’s goal is to improve AI data operations each year through 2026 and 2027. This long-term commitment underscores the importance of AI in modern grid management, particularly as energy demands become more unpredictable due to environmental factors and increased renewable energy sources.
Enhanced Forecasting Accuracy
The use of advanced AI models is another important step forward in load forecasting, beyond classical approaches. These models utilize non-linear constraints and are based on ENLSIP algorithm estimations, allowing them to handle complex data from over four million smart meters. Hydro-Québec’s spokesperson noted, “Having to deal with data from more than four million smart meters is another ball game.”
The AI models consist of several tens of functions with hundreds of parameters, which are regularly adjusted for optimal performance. Such intricate modeling enables AI to anticipate unusual demand patterns during extreme weather events, such as heatwaves, thereby ensuring that electricity supply aligns with customer needs.
Vijaykar, a grid management veteran, noted that AI’s impact goes beyond just saving time and dollars. He stated, “At a minimum, AI can help to run systems more efficiently. At a maximum, it will avoid catastrophic blackouts.” This capacity to enhance reliability has established AI as an immeasurable tool in energy management.
Proactive Grid Management
AI is completely changing the way that utilities are able to manage their grids, providing proactive energy distribution solutions. Through its intelligent and advanced demand forecasting capabilities, it delivers utilities the tools they need to effectively respond in real-time to volatile and changing demand scenarios. This flexibility gives utilities something like a check on their spending power, enabling a more dynamic management of energy resources.
As the penetration of renewables continues to grow, grid operations have become a more challenging task, especially given the variable nature of renewable energy generation. David Adkins noted that “the increasing penetration of renewables introduces variability and uncertainty into grid operations, making traditional forecasting and control methods less effective.” By integrating AI, utilities can respond to these challenges with greater accuracy and speed, allowing them to stay resilient even in the most unpredictable circumstances.
Vijaykar commented on the responsiveness AI provides, stating, “We can receive signals from the grid and spin up into action in a very short amount of time with very limited advanced notice from the utility.” This core functionality unlocks utilities to provide targeted and timely load shapes and load reductions.
A Collaborative Approach
Though AI presents major benefits in load forecasting, professionals warn it shouldn’t completely replace traditional forecasting methods. Adkins highlighted this concern, stating, “AI complements but does not replace grid planning and forecasting.” He noted that AI-driven orchestration works best when it is complemented by human oversight and market signals. Together, they paint a much more flexible and resilient toolkit.
While the path to realizing the full benefit of AI integration will have its bumps, Clermont noted that the training of AI systems is crucial for their success. “When they get confident, Hydro-Québec will stop using the old model. It is a training question. The AI needs to be trained.” He admitted that at first performance shy away from expectations, but as time passes, the models begin to almost double in improvement.
Hydro-Québec is currently preparing to scale up its use of AI in load forecasting to over 350 substations by 2028. That’s why the company embraces both the challenges and opportunities that this new technology presents. Vijaykar remarked, “Load forecasting has always been an important function. AI is both a challenge and an opportunity.” This duality represents the changing landscape of how we manage the grid as utilities are beginning to realize the potential of new technologies and implement new strategies.