Hydro-Québec is leading the charge in the modernization of load forecasting and management through the advanced implementation of artificial intelligence (AI). This initiative has dual goals of improving grid reliability while being more operationally efficient. In 2018, the utility launched its AI journey on a limited scale with a proof of concept. They achieved all of this using a simple feedforward neural network model to forecast loads at a single substation. Today, they’re rolling this technology out to over 350 substations by 2028.
Hydro-Québec’s AI models are particularly effective in managing a fluctuation in demand patterns. Above all, they give a realistic picture to both short-term and long-term needs. Hydro-Québec has reached remarkable precision by combining new algorithms with machine learning. More specifically, they use non-linear models grounded in the ENLSIP algorithm estimations. The system can provide 42 days of hourly forecasts by analyzing historic weather data, thereby proactively managing the grid and reducing operational costs.
AI not only increases the precision of forecasting, but brings the capacity for greater flexibility with the changing energy future. This innovative technology is at the forefront of preventing blackouts and keeping the power supply reliable. As Hydro-Québec explores the possibilities of AI, the future for energy management is exciting and full of potential.
Advancements in AI-Driven Forecasting
Hydro-Québec relies on advanced AI models. These models rely on thousands of functions and are tuned with hundreds of parameters. Forecasting accuracy would be significantly enhanced through the integration of these models. Hydro-Québec currently employs AI for short-term forecasting within a 36-hour window and for hourly forecasts up to 10–12 days ahead using meteorologists’ predictions.
One example of how well the AI performed was during a recent heatwave, where the technology was able to predict a rare demand pattern. This capacity to frustrate unplanned load spikes is a testament to the power of AI in load forecasting. Hydro-Québec’s spokesperson remarked, “Having to deal with data from more than four million smart meters is another ball game.” This remark illuminates the huge, diverse amount of data that needs to be synthesized in order to successfully manage energy loads.
Hydro-Québec is not resting on its laurels. They’re in the midst of developing a renewable energy forecasting prototype that will be deployed in 2026 and 2027. The utility’s statewide approach — featuring a bottom-up regional strategy for load forecasting — will help meet that goal, laying the groundwork for improved operational efficiency.
The Challenges and Opportunities of AI Integration
Even with promising technologies on the horizon, the challenge lies in incorporating AI into systems that already exist. Utilities such as Hydro-Québec are naturally risk-averse, as any misstep no matter how small can lead to catastrophic outcomes. Vijaykar, an industry expert, noted, “Utilities are understandably conservative about the system. Unlike the tech industry, which can afford to move fast and break things, for utilities one mistake can be catastrophic.”
One of the unique challenges AI does present is its complexity. Clermont, an AI specialist, explained, “It is a training question. The AI needs to be trained. On the first day, it is probably not that good, but after a year, it is probably better than you.” This statement goes on to emphasize the critical role of comprehensive training and data maintenance in achieving successful AI deployment.
Additionally, Vijaykar admitted that with all the challenges and opportunities presented through AI, the technology can really help improve operational efficiency by leaps and bounds. He stated, “Load forecasting has always been an important function. AI is both a challenge and an opportunity on both sides of that equation.” The ability of AI to prevent widespread, catastrophic blackouts only heightens the urgency of incorporating this powerful paradigm into today’s grid management.
Future Directions and Continuous Improvement
Beyond scaling up, Hydro-Québec has a goal of bettering its AI practices. Further, the utility said it would improve its use of smart meter data and forecasting capabilities by leaps and bounds. This continued progression will help them to seamlessly respond to rising and falling energy needs while keeping the grid balanced.
The future of AI integration into load forecasting is more complementary than it is a zero-sum replacement. As Adkins pointed out, “AI complements but does not replace grid planning and forecasting. AI-driven orchestration works best alongside grid forecasting, market signals, and human operational oversight.” Further, this perspective highlights the importance of a comprehensive approach that puts the latest technology to work alongside the expertise from decades of practice.