Broad sector diversity
Captured across multiple sectors: hospitality, education, retail and foodservice, and sports and venues. This diversity exposes algorithms to the full spectrum of real-world usage.
Data Asset
The operating-data asset for smart products
NoWatt owns an operating-data asset built on two decades of change-based electrical consumption across diverse sectors and appliances. Its value lies in the resolution and breadth of estate and appliance coverage. It provides the field history a manufacturer needs to train self-monitoring equipment from day one, rather than waiting years for its own fleet to generate it.
Why it matters
Lab data shows expected behaviour. NoWatt's high-resolution data exposes what products actually encounter in the field.
High resolution
Change-based electrical-consumption data, timestamped to the second, that captures the energy signatures of degradation, drift, and misuse while keeping twenty years of history tractable.
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
Operating history since 2006
Sectors
10+
Hospitality, education, retail and foodservice, sports and venues, and more
Organisations
75+
Large operators and multi-site estates
Devices monitored
100,000+
Appliances resolved, largely via disaggregation from metered points
Data points
100bn+
Stored readings, change-based — volume reflects real activity, not fixed-rate padding
Sensors deployed
20,000+
Deployed across sites and assets over the full 20 years (cumulative, not concurrent)
Diversity and resolution: twenty years of change-based electrical consumption from real buildings and infrastructure — something a lab cannot generate.
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.
The data is well suited to training smart, self-monitoring equipment to recognise 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 do we train embedded intelligence for a product line before we've shipped anything to learn from?
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.
Large building portfolios
Education
Seasonal occupancy patterns and diverse building types add unusual variability that sharpens the analysis model.
Diverse infrastructure
Facilities
Wide asset variety across managed estates adds cross-category operating behaviour to the benchmark.
Customer-facing estates
Retail
HVAC, refrigeration, and catering equipment across high-footfall sites with strong operational consistency requirements.
What we provide
You licence the asset; we partner with you to apply it
NoWatt owns this operating-data asset and licenses access to it on a non-exclusive basis. The data is raw and supports many applications, so we work alongside your team — typically a short-to-medium-term partnership — to shape it into the capability you need, whether that is fault and diagnostic models, warranty evidence, or product-design insight.
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.