Artificial intelligence for Kazakhstan's power sector

In the power sector, AI addresses four classes of problems: load and imbalance forecasting for the balancing market, detection of commercial losses in distribution grids, predictive diagnostics of substation equipment from dissolved gas analysis (DGA) data, and optimization of boiler-unit regimes at combined heat and power plants. KEGOC already uses an AI consumption forecast with accuracy of up to 97% — the same class of solutions is available to regional companies.

Industry challenges and our solutions

Pain

Distribution grid losses of 8–15% against a 6–7% norm; the difference is booked as a loss by the grid company

Solution

Balance analysis by feeder and transformer substation + ML scoring of consumers by theft probability + routing of inspector raids

Raid confirmation rate up from <10% to 40–60%, recovery of 20–30% of commercial losses

Pain

Consumption forecast errors of 3–5% — direct imbalance penalties on the balancing market

Solution

ML forecast of load and renewable generation (day-ahead and intraday) with a probabilistic imbalance-risk estimate

Error down to 1.5–2.5%, −40–60% imbalance costs

Pain

Aging power transformers: a failure means $1–3 million for a replacement and a 12–24-month factory queue; diagnostics by oil sampling once every 6–12 months

Solution

Fleet health index from DGA history and load regimes: risk ranking, an investment program justified by data

Major failures prevented, 10–15% of the refurbishment budget reallocated effectively

Pain

Manual combustion control at coal-fired CHP plants: 1–3% fuel overconsumption, emission exceedances

Solution

Advisor for the optimal fuel/air ratio and burner distribution, slagging forecast

+0.5–1.5% boiler efficiency, lower environmental charges

Typical solution architecture

  1. Sources: automated metering system (AMI), SCADA/telemetry, DGA logs, weather data, balancing market data
  2. On-premise data mart with a grid balance model
  3. ML platform: forecasting models, loss scoring, equipment health index
  4. Interfaces: loss map, inspector's mobile app, chief engineer's dashboards

Solutions for the industry

Predictive equipment maintenanceDigital twins and process optimizationComputer vision for industrial safety (HSE)Private LLM assistants inside the enterprise perimeter

Why Kazakhstan's power sector has the clearest window for AI

Wear on KEGOC's transmission grid is 62.8%; at regional power grid companies it is 70% and higher. The 2025/26 autumn-winter peak reached 17.7 GW with an expected deficit of up to 1 GW. By 2035, 7,000 km of new power lines must be built and 10,500 km refurbished — and investment must be allocated by the actual condition of assets, not “by age”.

The system operator is already advanced: SCADA/EMS with AI forecasting of consumption and losses (accuracy up to 97%). Regional power grid and energy supply companies, however, lag years behind: losses of 8–15% against a 6–7% norm, “same as yesterday” forecasting in Excel, transformer diagnostics by oil sampling once every 6–12 months. The difference between actual and normative losses is booked as a direct loss by the grid company — and this is exactly where AI pays back within months.

How AI finds electricity theft

Most regional grid companies already have automated metering (AMI) data at transformer substations and partially at consumers. Balance analysis pinpoints the grid sections where energy is “lost”; an ML model ranks consumers by the probability of a violation — from consumption patterns, comparison with neighbors on the same feeder, seasonality and the history of inspection reports. Inspectors receive ranked assignments in a mobile app with photo capture instead of a “blind” inspection round.

The effect is crystal-clear to measure — additional billings and lower imbalance — so the project can run on a success-fee model. Grid companies also have a source of digitalization funding: the “Tariff in exchange for investment” program.

How transformer condition is forecast without online sensors

Every grid company has an undervalued asset — decades of laboratory dissolved gas analysis (DGA) logs. An ML model interprets the gas trends (Duval triangle plus degradation-trajectory forecasting), matches them with load regimes from SCADA and builds a health index for every unit. The fleet is ranked by risk; online DGA monitors are installed selectively — only on confirmed top risks.

This inverts the logic of the investment program: instead of replacement “by age”, replacement by actual condition, justified to the regulator with data rather than expert opinion.

Roadmap for a regional grid company

  1. Express analysis (2 weeks): balances of 10 feeders plus an AMI export — a map of anomalous transformer substations and an estimate of commercial losses in tenge (KZT)
  2. Loss pilot (3–4 months): one grid district — consumer scoring, ranked inspector assignments, measurement of raid confirmation rate and additional billings against the baseline
  3. In parallel — a transformer retrospective (8–12 weeks): digitization of DGA logs, fleet health index, material for the investment program and tariff application
  4. Scaling: all grid districts, integration with billing and GIS, load forecasting, SLA support

FAQ

Frequently asked questions

What chief engineers, security and procurement teams usually ask — with straight answers.

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Next step

Request a feeder balance analysis

Tell us about your task — we'll come back with a data audit plan and an effect estimate within two business days.

  1. 1A 30-minute call: the task, the data, who signs off on security
  2. 2Data audit or a retrospective analysis on an anonymised export
  3. 3An 8–12 week pilot with success criteria agreed before the start
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