Driving the Future of Metering: How KEW is Revolutionizing Energy Meters with AI
In today’s rapidly evolving power landscape, energy meters are no longer just billing devices — they’re becoming intelligent nodes that monitor, learn, and even predict. At KEW, we are leading this transformation by integrating Edge AI into our next-generation energy meters.
With AI capabilities built directly into the meter hardware, KEW meters don’t just measure energy — they understand it. And this shift is solving some of the most critical challenges in the power and energy sector.
Let’s explore how AI — particularly Edge AI — is helping KEW solve real-world problems in the metering industry, across five powerful use cases.
🔍 1. Anomaly Detection: Catch What Traditional Systems Miss
Traditional meters rely on simple rule-based logic — like alerting when voltage exceeds 250V or current drops below 1A. But this threshold-based system often misses subtle yet important deviations that indicate system inefficiencies or faults.
At KEW, our AI-powered meters learn the normal operating profile of your electrical system over time. For instance:
Voltage usually stays around 230V
Current typically fluctuates between 2–5A
Energy spikes in the morning and evening in commercial setups
Now, imagine it’s a busy weekday afternoon at a retail store — but energy usage suddenly flattens, and current drops to 3.1A, without crossing any alert threshold. Traditional systems stay silent.
But a KEW smart meter detects the deviation instantly and flags it as an anomaly, indicating possibilities like:
A failing device
A loose neutral wire
Or even energy theft
That’s the power of adaptive, AI-based anomaly detection — smart, real-time alerts rooted in pattern understanding, not fixed limits.
🧠 2. Pattern Recognition: Making the Meter Situationally Aware
KEW’s AI-enabled meters don’t just read numbers — they recognize behavior.
Every electrical load creates a usage pattern. A coffee machine in the morning, lower loads during work hours, and kitchen appliances buzzing in the evening — all these form recurring signatures. Our meters recognize these patterns, learn them, and detect when something breaks the norm.
Let’s say an unfamiliar device is suddenly plugged in during a typically low-usage hour. A traditional meter might miss it.
But KEW’s Edge AI recognizes this as a new, irregular pattern — and can identify:
When the pattern occurred
Whether it’s correlated with voltage drops
And if it’s impacting power quality
This allows operators to take actionable decisions, reduce inefficiencies, and optimize usage — all in real-time.
🛠️ 3. Source Identification: Know the Problem and Its Origin
Detecting an issue is one thing — pinpointing where it’s coming from is another.
In a co-working space like WorkNest, where multiple electronics are running — laptops, 3D printers, HVAC, coffee machines — a spike in harmonic distortion on Phase B could disrupt sensitive devices.
A traditional meter may just say:
“⚠️ Harmonic distortion detected.”
But KEW’s AI-enabled meters go deeper:
“⚠️ Abnormal harmonics are originating from the 3D printer in the prototyping bay on Phase B.”
This level of real-time source identification transforms the meter into a diagnostics tool.
No guesswork. No downtime. Just fast, targeted troubleshooting.
⏳ 4. Time-Series Forecasting: Anticipate Demand Before It Hits
Energy consumption isn’t static. It’s seasonal, cyclical, and time-dependent. Forecasting this accurately can prevent overloads, enable load balancing, and ensure optimized power distribution.
Traditionally, this was done in the cloud using vast historical data — which came with:
High infrastructure costs
Latency issues
Privacy concerns over user data
At KEW, we bring this forecasting right to the device.
Our meters use local historical data and Edge AI models like LSTM or GRU to forecast:
The expected load in the next 60 minutes
Voltage drop risks during evening spikes
When batteries should pre-charge to optimize solar usage
This is especially useful in microgrid and distributed energy systems, where local intelligence is key.
🔧 5. Predictive Maintenance: Prevent Breakdowns Before They Happen
One of the most transformative applications of AI in energy meters is predictive maintenance.
Let’s say a refrigerator’s startup voltage drop is getting steeper. The current draw has increased subtly. And some low-level harmonics are building up.
Individually, these might look like noise. But KEW’s embedded AI models recognize this as a pattern of degradation — a signal that something is about to fail.
With algorithms like Random Forest or Cox Regression, our meters can estimate:
Which device is likely to fail
What component may be degrading
When maintenance should be scheduled
The result?
Lower downtime. Optimized service schedules. No more reactive firefighting.
💡 KEW: Building the Next Generation of Smart Energy Intelligence
At KEW, we’re not just manufacturing meters — we’re building intelligent systems that sense, learn, and act.
By bringing AI to the edge, we are:
Making energy meters proactive, not just reactive
Solving real-world problems with real-time intelligence
Enabling smarter power usage for industries, buildings, and utilities
🔗 Explore our AI-enabled metering solutions and see how KEW is shaping the future of energy management.
✅ Ready to future-proof your metering infrastructure?
📩 Contact us for demos, partnerships, or to deploy AI-powered meters in your facility.
🔎 Visit our product page or reach out at [email@email.com].
