Unrivalled diversity
Captured across multiple sectors: hospitality, education, manufacturing, logistics, and retail. This diversity exposes algorithms to the full spectrum of real-world usage.
Data Asset
The definitive learning dataset for smart products
NoWatt’s dataset is built on two decades of continuous electrical consumption across diverse sectors and appliances. Its value lies in the resolution and breadth of estate and appliance coverage. It provides the ultimate foundation for training self-monitoring equipment.
Why it matters
Lab data shows expected behaviour. NoWatt's high-resolution data exposes what products actually encounter in the field.
High resolution
Continuous, high-frequency electrical consumption data that captures the exact energy signatures of degradation, drift, and abuse.
20 years of history
You cannot simulate 20 years of slow equipment failure in a lab. The dataset contains two decades of true lifecycle degradation and intervention patterns.
Scale and provenance
Value comes from breadth, duration, and real-world operation
This is not synthetic data. It comes from real-time monitoring across organisations, operating conditions, and environments, captured over a long enough period for long-term behaviour to become meaningful.
Years of data
20
Continuous real-world operating data since 2006
Sectors
10+
Hospitality, education, manufacturing, utilities, and more
Organisations
75+
Large operators and multi-site estates
Devices monitored
100,000+
Appliances, systems, and monitored endpoints
Data points
100bn+
Captured across two decades of real operation
Sensors deployed
20,000+
Installed across sites, assets, and infrastructure
Unrivalled diversity and resolution: No lab can generate this data. Twenty years of continuous electrical consumption from real buildings and infrastructure creates the ultimate learning dataset.
The data captures true machine and human behaviour through high-resolution energy signatures — exposing slow degradation, settings drift, misuse, and intervention patterns from varied operating environments.
This makes the data uniquely capable of training smart, self-monitoring equipment to recognise its own faults, classifying whether a problem lies in the asset, the installation, or the way it's being used.
What it contains
More than data points: context, behaviour, and consequences
Manufacturers often have only partial visibility into how equipment behaves. This dataset adds the context that changes how those signals are interpreted: environmental variation, settings drift, maintenance, misuse, and the ways products are actually used after deployment.
How does this product actually behave after deployment across different operating environments?
Which anomalies are likely to be product faults, and which are more likely to come from installation or usage?
What patterns increase cost of ownership, service friction, or unnecessary intervention?
Where could better intelligence improve product design, alerting, or self-diagnosis?
How it was built
The value comes from the history, not the deployment itself
NoWatt spent years instrumenting and interpreting live operating environments. That matters because it explains how the dataset was built - and why it’s difficult to reproduce. The commercial opportunity today isn’t generic monitoring deployment, but what that accumulated operating history can now do for manufacturers.
01
Capture live operating behaviour
High-resolution operating data is captured in real-time, so performance can be understood in the context of load, weather, seasonality, and human behaviour.
02
Compare against the benchmark
Live data is compared against twenty years of real-world operating behaviour across sectors, sites, and hundreds of thousands of devices.
03
Classify the real cause
The benchmark shows whether the issue sits in the equipment, the installation, or the way it is being used, so teams know what to fix first.
Real world diversity
Every sector adds useful variation to the benchmark
Manufacturers benefit because different operating environments create distinct stress patterns, usage signatures, and failure scenarios. The more diverse the data, the more valuable the comparative context becomes.
Multi-site estates
Hospitality
High appliance density, 24/7 operation, and consistent fault patterns make this one of the richest sectors in the dataset.
Process equipment
Manufacturing
Process loads, duty cycles, and wear patterns across plant and production equipment build a strong benchmark for anomaly detection.
Large building portfolios
Education
Seasonal occupancy patterns and diverse building types add unusual variability that sharpens the analysis model.
Continuity-critical assets
Logistics
Refrigeration, heating, and building services in supply-chain environments where unplanned downtime has direct commercial cost.
Next step
See how manufacturers can apply the dataset
The next question is not whether the benchmark exists. It is how that operating history could improve diagnostics, service, and product performance in your category.