AI-powered HVAC analytics

Predictive Maintenance That Prevents Costly HVAC Failures

Detect equipment faults 20 to 30 days before critical failure. Our multi-signal ensemble engine monitors chillers, RTUs, and AHUs with 95 to 99% accuracy, turning reactive repairs into planned interventions.

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WattNest Fleet Monitor Live UNITS 4 AVG HEALTH 71.8% ACTIVE ALERTS 2 EST. SAVINGS $10.5K CH Chiller-01 FAULT 34% Condenser Fouling 94.2% TCA 7.8F TCO 101F kW 198 RUL: ~21 days R1 RTU-01 WARNING 68% Refrigerant Undercharge 72.8% SUCT 97psi DISC 312 R2 RTU-02 NORMAL 94% No Faults Detected 98.7% DMPR 45% SA 55F OA 72F AH AHU-01 NORMAL 91% No Faults Detected 99.1% SAT 55F RAT 72F MAT 63F
$7,800+
Savings per prevented failure
95-99%
Fault detection accuracy
20-30
Days advance warning
$7,800+
Savings per prevented event
REACTIVE VS. PREDICTIVE MAINTENANCE EMERGENCY REPAIR $12,000 - $18,000 Unplanned compressor replacement + overtime labor + lost productivity 3 to 7 days downtime VS. EARLY INTERVENTION $2,800 - $4,200 NET SAVINGS $7,800 to $15,200 per event PLUS YOU AVOID: Unplanned downtime costs Wasted energy from degradation Shortened equipment lifespan Tenant complaints / SLA penalties

HVAC Failures Cost More Than You Think

A single chiller compressor failure can cost $12,000 to $18,000 in emergency repairs, overtime labor, and lost productivity. Most building operators only find out when tenants start complaining.

  • Unplanned downtime costs $5,000 to $50,000 per incident depending on facility type
  • Degraded equipment wastes 15 to 30% more energy before visible symptoms appear
  • Running equipment to failure can reduce its total lifespan by 20 to 40%
  • Early intervention with WattNest brings repair costs down to $2,800 to $4,200
Our Technology

The Technology Behind Early Detection

Three pillars of intelligent fault detection that work together to catch equipment problems weeks before they become emergencies, while eliminating false alarms.

Multi-Signal Ensemble Engine

Five independent detection signals cross-validate every alert: changepoint detection, Mann-Kendall trend analysis, sigmoid fault probability curves, Gaussian Process regression forecasting, and survival analysis. A fault is only flagged when multiple signals agree, eliminating false positives.

Physics-Informed ML Models

Utilizing advanced time series machine learning algorithms and traditional FDD methods, grounded in thermodynamic degradation models. Built-in explainability shows you exactly which sensor readings triggered an alert and why, so your technicians know what to inspect.

Edge + Cloud Architecture

Lightweight anomaly scoring and detection algorithms run locally on-prem with no data leaving the site. When edge devices detect early signals, advanced GP forecasting and survival analysis run in the cloud for more advanced capabilities. BACnet/IP and Modbus connectivity for universal HVAC compatibility.

Real Detection Results from Live Deployments

Our models have been validated on industry-standard benchmark datasets and real-world field data. In live deployment, the multi-signal ensemble approach consistently detects faults weeks before they become critical, with zero false positives to date.

99.3%
Chiller detection accuracy
0
False positives MTD
21
Day advance warning
CHILLER-01 PREDICTION TIMELINE Condenser fouling detected 41 days before critical failure WARNING CRITICAL NORMAL Day 0 Install Day 40 Anomaly onset Edge detected Day 60 Warning crossed GP forecast active Day 81 Critical if unaddressed ~21 day RUL CI: 9 to 47 days | Mann-Kendall p<0.001

Validated on industry-standard benchmark datasets and real-world field data

Technical Specifications

Built for Enterprise HVAC Infrastructure

Protocols

BACnet/IP, Modbus RTU/TCP, REST API

Connectivity

WiFi, Ethernet, 4G LTE

Edge Hardware

Low-cost edge device, lightweight model footprint

ML Models

ML-powered classification, Gaussian Process, anomaly scoring

Analytics

Feature contributions explainability, RUL estimation, 95% CI predictions

Security

TLS 1.3, encrypted at rest, SOC 2 ready

Equipment Coverage

Comprehensive HVAC Fault Detection

Purpose-built detection models for the three most critical commercial HVAC equipment types, with fault-specific ML classifiers validated on industry benchmark datasets.

Chillers

Centrifugal, screw, and scroll compressor chillers. Detect condenser fouling, evaporator fouling, refrigerant issues, and compressor degradation. Utilizing advanced time series machine learning algorithms and traditional FDD methods with 99.3% accuracy across 7 fault types.

Rooftop Units

Single-zone and multi-zone RTUs from all major manufacturers. Detect refrigerant undercharge, economizer faults, stuck dampers, and fan bearing wear. Validated on industry benchmark FDD and field data.

Air Handling Units

VAV and CAV air handling units. Detect coil fouling, damper faults, supply air temperature anomalies, and mixed air issues. ML-powered classification utilizing advanced time series machine learning algorithms and traditional FDD methods with 96.7% accuracy.

For Service Companies

Built for HVAC Service Companies

A white-label predictive maintenance platform that transforms your service offering and creates new recurring revenue streams.

Your Company Fleet Dashboard White-label Live Downtown Office Tower 6 units monitored 1 FAULT Avg Health: 74% Savings: $8.2K Dispatch recommended Regional Medical Center 12 units monitored ALL OK Avg Health: 93% Savings: $14.6K No action needed Eastside Data Center 8 units monitored 1 WARN Avg Health: 89% Savings: $11.3K Monitor closely Dispatch Queue CH-03 Condenser Fouling Office Tower | RUL: 18d RTU-05 Refrig. Low Data Center | RUL: 32d W White-Label Platform Your branding, your domain Deploy under your company name. Customers see your brand. New recurring revenue stream. 26 Fleet Management All customer sites, one view Monitor every unit across every customer from a single dashboard. Scales with your business. ! Prioritized Dispatch Urgency-based work orders Automatically prioritize service calls by remaining useful life and equipment criticality. $ Revenue Opportunity Add predictive to your offering Transform from reactive service calls to proactive monitoring subscriptions.
Use Cases

Predictive Maintenance for Every Facility

CRAC 99.999% Uptime target 24/7 Monitoring Critical cooling

Data Centers

99.999% uptime for critical cooling infrastructure. Detect CRAC and chiller faults weeks before they threaten server room temperatures.

Reduce costs 30%+ Maintenance savings Extend life 5+ yrs Equipment longevity

Commercial Buildings

Reduce maintenance costs by 30% or more and extend equipment life by years. Catch problems before they affect tenant comfort or trigger SLA penalties.

SERVICE Add to your offering Predictive Maintenance as a subscription service Recurring revenue Monthly monitoring fees per unit per month

HVAC Service Providers

Add predictive maintenance to your service offering. Generate recurring monitoring revenue while delivering better outcomes for your customers.

Ready to Predict and Prevent HVAC Failures?

See how WattNest can detect equipment faults 20 to 30 days before critical failure. Whether you manage a single building or a fleet of customer sites, we can help.

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