Smarter Cooling: AI and Super-Efficient ACs in 2026

PJM’s summer peak demand in 2025 was the highest it’s been since 2011. That’s not a trivia fact — it’s a warning flare. When heat and humidity push air-conditioning demand into the stratosphere, utilities don’t just see bigger bills and stressed transformers. They see reliability risk, emergency operations, and a planning headache that’s getting harder to model year over year.

Most companies still treat cooling as a simple efficiency problem: buy a higher-SEER unit, call it progress. That approach is outdated. The real issue is how cooling behaves in the real world — especially in hot-humid climates where humidity control can quietly double energy use through overcooling.

The most useful signal from 2025 isn’t “we need more efficient air conditioners.” It’s this: we now have credible field evidence, emerging global performance benchmarks, and a clear opening for AI in energy and utilities to scale smarter cooling without breaking the grid.

2025 proved the grid can’t “just add more cooling”

Answer first: 2025 showed that peak demand is being shaped by cooling load in a way that traditional grid planning and building efficiency programs can’t keep up with.

Across regions — from North America to South and Southeast Asia — hotter, stickier summers drove record or near-record electricity demand. In several markets, reliability issues weren’t theoretical: prolonged blackouts and strained grids became part of the lived experience. Cooling is increasingly a public-health requirement, but it’s also a peak-demand multiplier.

Here’s the cycle utilities are stuck in:

• Hotter summers drive more AC adoption and longer run-times
• More AC load drives higher peak demand
• Higher peak demand forces dirtier peakers and grid upgrades
• More emissions and waste heat contribute to more warming

Breaking that loop takes more than incremental appliance efficiency. It takes system-level cooling: better building envelopes, reflective roofs, smarter controls, and air conditioners that manage temperature and humidity together.

For readers following our AI in Energy & Utilities series, this is familiar territory: once load becomes volatile and weather-driven, the winners are the organizations that combine demand forecasting, flexible load management, and device-level intelligence.

Why “rated efficiency” keeps disappointing in the field

Answer first: Air conditioner ratings often don’t reflect real operating conditions, especially humidity, part-load behavior, and how people actually use AC.

Most AC performance metrics were built around lab assumptions that miss what drives real bills:

• Part-load operation (units cycling on/off instead of steady-state)
• High latent loads (humidity removal) in hot-humid climates
• Thermostat behavior and comfort choices
• Poor installation, refrigerant charge issues, and airflow problems

The result: utilities and consumers pay for “efficient” equipment that doesn’t deliver expected savings during the hours that matter most.

A sharp example emerging from 2025 work in the cooling sector: overcooling to control humidity. If a system can’t dehumidify effectively at a comfortable temperature, it often drives the coil colder than needed, then reheats or just leaves the room colder than occupants want. That’s wasted energy — and it shows up right at peak.

One line I keep coming back to from industry leadership this year:

“Overcooling to manage humidity drives excessive energy use — even in the most efficient units. Managing humidity and temperature together is key.”

That’s the technical truth utilities need their efficiency programs to reflect. And it’s exactly where AI-enabled controls and better testing standards start to connect.

The most practical breakthrough: performance standards that match reality

Answer first: Updating test standards to include load-based performance and humidity requirements creates clear design targets — and makes utility incentives and planning models far more reliable.

A lot of “innovation” hype dies because it can’t be measured consistently. Cooling is no exception. What changed in 2025 is momentum toward test methods and metrics that better mirror real use.

Three developments matter for energy and utility stakeholders:

Load-based testing is moving from niche to mainstream

Load-based testing evaluates performance across operating conditions rather than a single idealized point. That matters because ACs spend much of their lives at part load, not full blast.

When standards incorporate load-based behavior, you get:

• More accurate savings estimates for efficiency programs
• Better inputs for peak demand forecasting
• Clearer product differentiation for procurement and bulk-buy programs

Humidity performance is becoming a first-class requirement

A sensible heat ratio requirement (a direct push to account for humidity control) is a big deal. It forces designs that can deliver comfort without the overcooling penalty.

Consensus-building is happening across research groups

Replicable methods are the bridge between lab insights and market adoption. When independent research groups align on testing approaches, utilities can trust the numbers enough to design incentives and capacity value around them.

If you work in utility planning, this is the unglamorous foundation that makes everything else possible.

Where AI makes cooling truly scalable for utilities

Answer first: AI turns cooling from a blunt load spike into a controllable, forecastable, verifiable resource — but only if it’s paired with device capability and good measurement.

Utilities don’t need “smart thermostats everywhere” as a slogan. They need three things that AI is good at: prediction, coordination, and verification.

1) Demand forecasting that understands cooling behavior

Traditional load forecasting struggles when heat waves bring nonlinear behavior: more devices switch on, setpoints drop, and humidity changes the runtime profile.

AI demand forecasting models can ingest:

• Weather (including dew point and wet-bulb indicators)
• Customer segmentation (building type, income, AC saturation)
• Device telemetry (compressor runtime, fan speed, coil temps)
• Historical event response (how customers actually behave during alerts)

The payoff isn’t academic accuracy. It’s operational confidence: staffing, procurement, and dispatch decisions become less reactive.

2) Control strategies that manage comfort constraints, not just kW

The best demand response programs fail when they treat people like batteries.

AI-enabled controls can shift from simplistic “raise setpoint 2°F” to strategies like:

• Pre-dehumidification before peak (so comfort holds longer)
• Coordinated fan/compressor staging to reduce latent load efficiently
• Adaptive setpoint bands that respect occupancy and comfort feedback
• Short-cycle avoidance to reduce wear and preserve efficiency

For hot-humid regions, humidity-aware control is often the difference between a program customers tolerate and one they opt out of.

3) Measurement and verification (M&V) that’s fast enough to matter

Utilities live and die by credible savings.

AI can automate M&V by creating counterfactual baselines (“what would this customer have used without the intervention?”) using interval data and contextual variables.

That enables:

• Faster settlement for demand response events
• Better program tuning mid-season
• Credible capacity value for flexible load portfolios

This is where AI in energy and utilities moves from pilot to planning asset.

What “super-efficient AC” really means — and why it’s a grid resource

Answer first: Super-efficient ACs aren’t just about lower kWh; they can deliver outsized peak reduction because they avoid the humidity-driven overcooling trap.

Field testing in 2025 added something the market badly needed: real-world comparisons between typical units, high-efficiency units, and next-generation approaches. The headline finding wasn’t “efficiency is good.” It was that performance gaps are meaningful enough to change peak planning assumptions.

Here’s how to translate this for utility and building leaders:

• A unit that maintains comfort with less overcooling reduces runtime during the most stressed hours
• Less runtime reduces feeder and transformer stress
• Lower peak reduces the need for peaker dispatch and emergency imports
• Reduced peaks cut total system costs, not just customer bills

There’s a procurement angle here that’s underused: utilities can treat super-efficient, humidity-competent ACs as non-wires alternatives, especially in growth corridors where distribution upgrades are imminent.

A 2026 playbook utilities can actually run

Answer first: The fastest path is pairing better equipment specs with AI-enabled operations: pilot, prove, scale — with data you can defend.

If you’re building a cooling strategy for 2026, here’s what I’d prioritize.

Step 1: Update your definition of “efficient” in programs

If your rebates and preferred equipment lists don’t account for humidity performance, you’re paying for savings you may never see at peak.

Practical actions:

• Require performance metrics that reflect part-load behavior
• Add humidity capability requirements for hot-humid territories
• Align incentives with verified peak reduction, not nameplate ratings alone

Step 2: Run scaled pilots across climates and customer types

One neighborhood pilot won’t generalize.

Design pilots to capture diversity:

• Hot-dry vs hot-humid vs mixed-humid service territories
• Apartment vs single-family vs small commercial
• Low-income households (where resilience benefits are highest)

Step 3: Treat controls as a product, not a feature

Many programs bolt controls on at the end. Flip that.

Look for platforms that can:

• Integrate AC telemetry and building sensors
• Optimize for both comfort and peak reduction
• Provide event-level M&V outputs utilities can audit

Step 4: Build the ecosystem: contractors, commissioning, and QA

The dirty secret of HVAC savings is that installation quality often decides outcomes.

AI can help here too:

• Fault detection from runtime and temperature signatures
• Predictive maintenance alerts (airflow issues, refrigerant charge problems)
• Contractor scorecards tied to verified performance

This is one of the cleanest “AI meets efficiency” use cases because it turns messy field work into measurable signals.

What to watch next: standards, adoption, and the 2030 target

Answer first: Expect 2026 to focus on scaling proof points, enabling manufacturers, and activating markets — with AI as the amplifier that makes performance dependable at scale.

The cooling sector has set an ambitious direction: by 2030, a meaningful share of the global room AC market should be served by super-efficient, climate-friendly technologies. That’s not going to happen from lab prototypes alone. It happens when three things line up:

• Standards that reward real performance (including humidity)
• Manufacturing capability to build and test what standards demand
• Market activation via finance, demand aggregation, and customer trust

AI fits because it reduces the friction in every stage:

• Better forecasting reduces grid risk and program uncertainty
• Smarter controls increase customer comfort and participation
• Automated M&V makes savings bankable

If you’re leading utility strategy, here’s the stance I’d take going into 2026: cooling is no longer just end-use consumption. It’s flexible capacity, public-health resilience, and a stress test of your grid intelligence.

The next heat wave will be brutal somewhere. When it hits your territory, will your cooling load behave like a surprise — or like a managed portfolio?