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Industry — 10

Energy & Utilities

The cost of an outage dictates the architecture.

targeted uptime
99.95%targeted uptime
operational data latency
<1soperational data latency
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Energy systems are designed around high availability, because an outage is not just lost revenue — it is lost public service. Add data sovereignty requirements and the right answer is often an on-premise deployment that sends nothing outward.

In short

Where do energy companies use AI?

Energy companies use AI in three places: intra-hour and day-ahead load forecasting, loss-and-theft detection, and predictive maintenance on grid assets. All three share the same real-time data platform. Neuros usually deploys inside the company network: meter and SCADA data never leave it, and the analytics side can only read from the operations network, never write to it.

What's hard in this sector

The cost of an outage dictates the architecture.

01

High availability

Even a planned maintenance window is narrow; zero-downtime deployment is mandatory, not optional.

02

Field data

Meter and SCADA data is high volume and noisy; uncleaned, it misleads every downstream analysis.

03

Loss and theft

Separating technical loss from theft requires anomaly detection over consumption behaviour.

What we can build for you

  • 01Load and generation forecasting
  • 02Loss and theft detection with field-crew dispatch
  • 03Predictive maintenance for grid assets
  • 04Customer self-service portal and mobile app
  • 05A real-time data platform for SCADA and meter data
  • 06On-premise AI deployment where data cannot leave

Sources

  1. 016698 sayılı Kişisel Verilerin Korunması KanunuT.C. Mevzuat Bilgi Sistemi · 2016
  2. 02AI Risk Management Framework (AI RMF 1.0)NIST · 2023

Frequently asked

Questions we get asked

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Error grows with the horizon, so a separate model is built for each. Intra-hour forecasting leans on measurement data, day-ahead on weather and calendar effects, and weekly on seasonality and tariff changes. Neuros builds a family of models split by horizon rather than one model, and tracks the error of each horizon separately.

The model produces a priority list, not an accusation. Neuros systems show every flagged connection with its rationale — which consumption pattern, which neighbour comparison, which period. No action is taken before a field crew verifies it, and every field outcome returns to the model, so false flags lower that pattern's weight at the next training.

Connecting directly does, which is why Neuros does not. Data moves from the operations network to the analytics network through a one-way transfer: the analytics side reads and cannot write back. No model that issues control commands runs on this path; a recommendation appears on the operator's screen and a human decides whether to apply it.

Other industries

Let's assess your sector specifically

This page is the general frame. Let's find the actual bottleneck in your operation together, in a 30-minute call.

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