We all want more efficient homes and buildings: they’re better for the planet and the grid, not to mention easier on the budget. The technology to make our spaces more efficient without sacrificing comfort or dramatically changing our habits already exists. Smart home and building technologies already support power-usage optimization, but we need AI to make those interventions scalable.

Solving the challenges of smart building integrations will require machine learning on the backend for pattern recognition, data ontology mapping, and reinforcement and optimization of data-based rulesets, as well as agentic LLM-powered user interfaces to accelerate device onboarding, enhance automation transparency and flexibility, and build trust and buy-in.

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Better Together: Smart Devices and AI

It seems like practically every new device has a network connection, and we can automate even legacy devices with a smart plug. Smart devices can be automated to:

  • Avoid drawing “vampire power” when they’re not in use.
  • Revert to a low-power “standby” mode after a period of inactivity.
  • Turn on or off based on occupancy, detected via motion sensors, cameras, or even cell phone proximity.

At first glance, cutting power to the TV or coffeemaker at bedtime might seem trivial compared to the massive challenges of climate change and grid stability. But these small interventions matter.

Keaton Chia, a researcher at the University of California San Diego’s DERConnect Testing Facility, has found that intelligent automation can reduce device power draw by 25 percent or more. “Small savings add up,” says Chia. “Attaching appliances like printers, TVs, and coffee makers to a smart plug can result in big energy savings at scale.”

“Scale” is the key. Even in a single home, there may be dozens of connected devices; a commercial building may have hundreds or thousands. Defining and managing rules for all of them presents challenges that AI is uniquely suited to solve.

Knowing What Is Automated

Before you can define energy-management rules, your building management system (BMS) or home automation platform has to know what devices are present and where they are. For households with many smart plugs, clear, human-readable names are essential for both people and AI (“Bedside Lamp – Guest Bedroom,” not “Smart Plug 5”).

Chia’s team is also closing the loop when devices change. By applying machine-learning to plug-level power signatures, they can detect when a device has been swapped out without notice and prompt an adjustment. “We wrote an algorithm that looks for a certain power-use signature,” he explains. “If that signature changes, we issue a warning to go check that device.” This keeps automations from silently failing when, say, a coffeemaker becomes a printer or a space heater shows up under a desk.

New tools are making device discovery far less manual. “With AI-power smart home assistance, adding a new device no longer feels like a project,” says Eric Smith, CTO of SAVI iQ. “The system instantly recognizes what you’ve connected, knows where it is, and applies the right rules automatically. Plug in a lamp, and the home assistant already knows it’s a lamp—no searching, naming, or programming required. With AI-driven discovery and context built in, your smart home or building doesn’t need constant babysitting. It just works.”

In the near future, BMS platforms will be able to identify and place a new device the instant it joins the network, automatically applying the right rules. AI will help by mapping messy, vendor-specific labels into standard ontologies so devices can “speak the same language” out of the box, eliminating brittle custom code and unlocking scale.

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Setting Smart Rules

Not all automation rules are created equal. With rules tuned to actual usage and human behavior, power management becomes far more impactful.

Thermostats illustrate the point. Most use a blunt rule: maintain a set temperature within a narrow band. AI allows for something richer. “By using data such as outside air temperature, humidity, and occupancy, reinforcement learning can better predict thermodynamics and adjust temperature setpoint accordingly,” explains Hsin-Yu Liu, an applied AI researcher at Articul8 AI. This multi-variable approach co-optimizes comfort and efficiency, avoiding the “all-or-nothing” cycling that wastes energy.

Historic data on occupancy and user preferences can shape initial schedules. Predictive inputs, like weather forecasts or event calendars, help the system anticipate. For instance, if a heat wave is expected, the building can pre-cool in the morning to avoid maxing out AC during peak demand. Reinforcement learning then fine-tunes over time. “If a user doesn’t touch the thermostat when it’s at 73 but frequently lowers it after sunset, the model can learn to adjust proactively,” Liu says.

In Liu’s research, data-driven control improved HVAC efficiency by about 16% compared to traditional rule-based policies, with no noticeable comfort penalties.

Sharing Data Across Devices

Efficiency compounds when devices share data and act in concert. Lighting occupancy sensors, for example, can tell thermostats which zones are occupied and signal smart plugs which devices actually need power.

Integrated automations expand the range of interventions. Blinds can automatically close to block afternoon sun, reducing cooling demand, while HVAC systems draw in cooler outside air when conditions allow. These “no-cost” strategies build efficiency without compromising comfort.

But deep automation requires interoperability. “Interoperability is hard, even with protocols like BACnet,” says Chia. “Labels don’t match, commands differ, and it makes integration incredibly time-consuming.” If data ontology were standardized, he argues, “we could focus on innovating at the application layer.”

Chia’s team has seen what’s possible when devices connect seamlessly. “Working with Z-Wave and Home Assistant was mind-blowing,” he recalls. “We could plug in all these very different consumer devices, and they just worked.” That kind of interoperability forms the foundation on which AI can add intelligence at scale.

Flexing for the Human Factor

Energy management relies on centralized rules—but people need flexibility. Chia emphasizes the importance of local visibility and override options: “If technology isn’t transparent, people get frustrated and pull the plug. You need prominent override buttons and clear feedback.”

Every override produces data that can be used to refine the rules, reducing the need for future intervention. Liu frames AI not as a replacement for human control, but as an assistant: “It’s not zero or one; it’s not fully automated or fully manual. You can let AI run overnight, then give users control during active hours. The balance matters.”

AI’s conversational capabilities enhance both transparency and flexibility. Large language models (LLMs) can help people understand what their smart devices are doing and why.

“Automation requires predictability, but humans want autonomy,” Chia notes. “If I don’t know whether my TV is off because it’s broken or because the smart plug thinks I’m not home, I’ll rip the plug out. LLMs create a bridge: I can ask, ‘Why are my lights off?’ and get a meaningful answer.”

Smith extends the vision: “A smart home assistant can be a true partner in helping you meet your energy goals. Instead of silently enforcing rules, it can actively make suggestions—delaying a dishwasher cycle to save money during peak pricing, or explaining that blinds are open because it’s a sunny day. By being transparent, automation shifts from feeling like an irritant to becoming a true collaboration.”

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Indeed, thanks to standardized interoperability platforms like Z-Wave, the smart home has become a proving ground for practical AI. Early adopters have shown how image recognition can be applied to trigger lights when a delivery arrives, or how summarization tools can distill dozens of motion or sensor events into a clear daily log. Homeowners can decide which functions stay under AI’s purview and which remain off-limits. For example, they may exclude AI from alarm systems while still using it to sort camera footage.

Crucially, these capabilities can run entirely on local hardware, preserving privacy and ensuring responsiveness even if cloud connectivity fails. The result is not AI dictating how a home runs, but AI acting as a customizable toolset that adapts to each household’s comfort level and priorities.

“AI can be a powerful tool in the smart home, but it should always be up to the household to decide how and when to make use of its abilities,” says Paulus Schoutsen, founder of Home Assistant. “We’ve seen people use image recognition to alert them when a package is dropped off and when a parking space in front of the house is free, or to turn all the home’s sensors into a quick daily summary of everything going on. At the same time, we believe it should be the choice of every home to decide what devices AI can control, for instance, not allowing it to control a smart lock or alarm system.”

Schoutsen continues: “Our community is also putting considerable effort into running AI locally, which we believe should be the future of the technology, as local control always provides a more private and resilient experience compared to the cloud. That balance between choice, transparency, local control, and privacy makes AI genuinely useful in the home.”

From Smart Devices to Smart Grids

AI doesn’t make homes “smart” in isolation; it turns them into active partners in a smarter grid. With discovery tools that know what’s plugged in, adaptive rules that balance comfort with efficiency, interoperable platforms that let devices work together, and conversational AI that keeps users in the loop, the building of the future isn’t just efficient. It’s participatory.

Every smart plug, thermostat, and sensor becomes part of a larger system that balances supply and demand, supports renewable integration, and strengthens resilience against disruptions. The payoff is more reliable power and buildings that are both more efficient and easier to live with. Sustainability without sacrifice is no longer aspirational: It’s achievable today by aligning AI, standards, and human trust.

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