How ecobee’s Donate Your Data Program is Playing a Role in Grid Resilience

One of the greatest hurdles to building a more resilient energy grid isn’t a lack of will, but a lack of precise modeling of residential energy behavior. Without knowing exactly how millions of homeowners interact with their heating and cooling systems in real-time, we are forced to rely on broad assumptions and guesswork. Armed with actual data, researchers can move past generalities to build demand models that reflect how people—and the grid—truly perform under pressure.

This is the "data gap" that the ecobee Donate Your Data (DYD) program was created to address. The DYD program invites ecobee Smart Owners to voluntarily share anonymized data from their thermostats with scientists and researchers all over the world who are answering critical questions related to the clean energy future.

Through ecobee’s unique Donate Your Data (DYD) program, over 200,000 Smart Owners share snapshots of their home energy use—like how warm or cool they keep their rooms, their daily schedules, when they bump the temperature up or down, and whether someone is home or away. All of this detailed information gives powerful insight into how people actually heat and cool their homes based on personal preferences and in response to weather conditions.

Today, Dr. Michael Blair, an Assistant Professor at Wilfrid Laurier University, is one researcher turning this granular data into a roadmap for grid resilience. In his latest research paper, “The Impact of Climate Change–An Empirical Analysis of Smart Thermostat Data,” co-authored with Saed Alizamir and Shouqiang Wang, Blair utilizes the scale and granularity of the DYD dataset to quantify the evolving requirements of the electrical grid.

We sat down with Blair to discuss the "inertia" of human behavior and why his findings suggest that tools like smart thermostats, time-of-use pricing, and tailored communication strategies will play a critical role in keeping the grid reliable for the future.

ecobee Citizen: What is grid resilience, and why is it important?

Dr. Blair: Grid resilience is a power system's ability to handle disruptions—to prepare for them, withstand them, and to quickly recover. It is important because a resilient grid is a more reliable grid. It’s a grid that can handle demands for electricity when unscheduled events occur, and demand suddenly changes. An obvious example is a heatwave. Heatwaves are no surprise, but their timing and intensity are not on a schedule. One way to handle unscheduled events when demand surges is to have lots of excess grid capacity. More power plants. Not always realistic. But another way is to find places in the system where energy demand has some “give”, some flexibility, where user demand can be modified. Charging electric cars overnight is an example of this. Finding this flexibility can help keep demand within a grid’s ability to deliver even when disruptions occur.

This is where ecobee’s DYD comes in. Among other things, we can use it to find some of the flexibility needed to keep grids resilient.

ecobee Citizen: Can you tell us what your research paper is about and what specifically you were trying to find?

Dr. Blair: Our research paper delves into understanding how residential energy use, particularly for heating and cooling, is influenced by weather and human behavior. While it’s intuitive to think that extreme temperatures drive energy consumption—like cranking up the AC on a scorching day—what’s less understood is how much people actually respond to these conditions and the variability in their reactions. That’s what we set out to discover.

Initially, we were curious about whether utility companies could leverage this understanding to optimize pricing or send targeted notifications, like through smart thermostats, to influence consumer behavior. But as we dug into the data, we realized something critical: people don’t always behave in predictable, rational ways. Traditional analytical models often assume optimal decision-making, but real-world data paints a much messier picture.

What made our study unique was access to detailed thermostat usage data, thanks to ecobee. Unlike monthly utility bills, which provide a vague snapshot of energy use, this granular data enabled us to observe how people actually adjust their thermostats in response to weather conditions. For example, if someone raises their AC setting, we could directly measure the impact on energy consumption, rather than guessing based on a general bill increase.

Ultimately, our goal was to strip away assumptions about how people should behave and focus on how they actually behave in response to weather changes. This approach not only sheds light on human behavior but also provides valuable insights for utility companies and policymakers aiming to design more effective energy strategies.

ecobee Citizen: The grid is now facing additional challenges, including the rise of data centers, electric vehicles, and industrial electrification. While residential heating and cooling may not directly compete with these sectors, it does contribute to overall electricity demand. How do these new drivers interact with the residential behavior you studied?

Dr. Blair: That’s a great question. While our study focused on residential cooling behavior using pre-pandemic data, the grid has become even more complex since then. One of the biggest changes is the rapid growth of data centers, fueled in large part by the rise of AI technologies. AI workloads require enormous computational power, which means data centers now demand a constant, massive amount of electricity 24/7. The growing adoption of electric vehicles is another significant new source of demand, especially during evening charging hours. Industrial electrification—like factories transitioning to electric-powered systems—further increases the baseline load on the grid. These sectors all draw from the same finite capacity, which creates overlapping pressures.

Residential cooling plays a unique role because it’s highly variable and weather-dependent. Unlike data centers or industrial processes, which have relatively steady demand, residential cooling spikes during heatwaves or at the end of the day when people return home. This variability makes it harder for the grid to balance supply and demand, especially when industrial and transportation loads are already taking up a large portion of the grid’s capacity.

What we found in our research is that creating "load flexibility" is critical to addressing these challenges. For residential cooling, tools like smart thermostats can help shift demand to off-peak hours—such as pre-cooling homes earlier in the day when the grid isn’t as strained. Similarly, EV charging can be scheduled for overnight hours to avoid overlapping with peak residential cooling. These small, targeted adjustments can make a big difference when scaled across millions of households.

Ultimately, our findings suggest that the grid’s future isn’t just about adding more capacity—it’s about understanding and influencing behavior to use what we have more efficiently. By combining smarter technology, better planning, and behavioral insights, we can balance these growing demands and ensure reliability for everyone, from industrial users and AI-powered data centers to individual households.

ecobee Citizen: Earlier, you mentioned the importance of time-shifting technologies in thermostats to help manage residential demand and reduce strain on the grid. At ecobee, we offer features like Time-of-Use scheduling, which allows thermostats to pre-cool or pre-heat homes during off-peak hours. How do you see features like this fitting into the broader effort to create load flexibility and manage demand?

Dr. Blair: Time-shifting technologies like ecobee’s Time-of-Use feature are critical for creating load flexibility. Pre-cooling or pre-heating during off-peak hours helps reduce strain on the grid during peak times, which is especially important as demand continues to grow.

What makes this approach so effective is that it’s proactive. Instead of the grid being overwhelmed during peak hours—like when people return home and turn on their cooling systems—these technologies shift that demand to times when the grid is under less pressure. 

What’s particularly valuable is that these solutions are automated. They don’t rely on users to make constant adjustments or decisions, which can be inconsistent or unpredictable. By removing that variability, these technologies ensure reliable, scalable benefits across millions of homes.

ecobee Citizen: Features like "Hold" and "Auto" can influence energy use. You found that these features, particularly "Hold," contribute to increased energy consumption during demand peaks. What did the data reveal about their impact?

Dr. Blair: The DYD data allowed us to observe a concept I call "Human Inertia." Think about it: you come inside on a hot day, still feeling the heat even though the AC is running. You use the manual “Hold” override feature to lower the thermostat to cool down faster—even though it doesn’t actually work that way—and then forget to adjust it back. That small, seemingly harmless action, when repeated across millions of homes, adds up to a significant energy drain.

This is where smarter software can make a difference—by guiding users back to energy-saving schedules in a way that feels natural and effortless.

ecobee Citizen: How would your research have been impacted if you hadn't had access to the DYD dataset?

Dr. Blair: Without the DYD dataset, this research simply wouldn’t exist. Traditional energy data, like monthly utility bills, only gives you a vague snapshot—how much energy was used, but not why or how. If your bill goes from $80 to $85, was it because you changed your thermostat, or was it the weather? You just don’t know. Analytical models can help generate projections by assuming people behave rationally—like adjusting their thermostat to balance comfort and cost—but humans are, like I said, messy and don’t always act predictably.

The DYD data was a game-changer because it allowed us to see the actual decisions people were making with their thermostats in real time—when they adjusted settings, how they responded to weather changes, and what patterns emerged. This let us validate and refine our models to better align with reality. For this kind of behavioral analysis across hundreds of thousands of homes, there’s no substitute.