utility loss analytics
The problem
Power distribution companies, gas utilities and water operators. It works with AMI and AMR data where you have it, and with billing and network data where you do not.
A typical situation
Our losses are well above the regulator's target, and every percentage point is revenue we cannot bill. We know some feeders, regions or districts are worse than others, but inspection teams still work from complaints and old lists. Meter data, billing and the network model sit in different systems, and nobody can tell us which inspections will actually recover money.
Outcomes
Indicative ranges, validated in your pilot. These figures come from comparable industry programmes, not from TashIT projects. Your pilot sets a baseline with you and measures the actual change.
Typically 1-3 percentage points lower in targeted feeders over 12–24 months
Basis: Feeder and transformer energy-balance programmes combined with targeted field action. Depends on the baseline loss level, metering coverage and enforcement capacity.
Typically 0.3-1 percentage point lower in targeted regions
Basis: Town border station and network balance analytics with meter-error and theft focus. Depends on measurement quality at source, network age and enforcement.
Typically 5-15% less non-revenue water in metered districts
Basis: District metered area programmes with leak detection and pressure management. Depends on network condition and how quickly leaks are repaired.
Typically 2-4 times more confirmed cases per inspection
Basis: Analytics-ranked inspection lists compared with complaint-driven or random inspection. Depends on how inspection lists are produced today.
From data to decision
We bring together the customer-to-network mapping from billing, GIS and asset records, AMI or AMR interval data, feeder and transformer meters, and town border station or bulk-flow meters.
Energy, gas or water balance per feeder, transformer, regulating station or district metered area, by period. Missing or wrong customer-to-network links are flagged for correction, because a balance is only as good as that mapping.
Models flag sudden consumption drops, tamper events, meter faults, flat or zero reads, unusual load profiles and balance gaps. Areas and accounts are ranked by estimated recoverable value.
Each case gets a recovery score and an evidence pack. Inspection lists reach field teams through a mobile app or your work-management system, and results come back to improve the models.
Loss trends by area are tracked against regulatory and internal targets, and recovered revenue is followed from inspection to bill to collection.
Data and integration
How the pilot works
Duration
Scope
Agreed in writing before work starts, with targets set against your baseline.
The pilot follows a short diagnostic and ends with a written results review. Scaling up is a separate decision, made on the measured results. How our engagements work
Solution blueprint
An energy-balance model that compares energy sent to each feeder and transformer with energy billed, then ranks loss hotspots so field teams go where recovery is most likely.
Status: Reference architecture and pilot plan, available for a paid proof of concept. Reference material, not a client project.
Where it applies
Keep exploring
Pilot 10–16 weeks
Condition monitoring and telematics analytics that predict failures and lower maintenance and fuel cost for plants and fleets.
Pilot 8–12 weeks
AI assistants and agents connected to your data and systems, deployed privately when data must stay in-country.
Pilot 8–12 weeks
Sub-metering and forecast-based dispatch of grid, captive generation, solar and battery storage to cut energy cost per unit.
Operations leaders buy lower energy cost, less leakage, compliance data they can trust and faster use of new capacity. Why AI projects stall without data and process foundations, and how a paid pilot with agreed measures works.
5 min read
Many industrial sites now run grid power, captive generation, solar and batteries side by side, scheduled largely by hand. What a dispatch optimiser does, the data it needs, how a pilot runs and what savings are realistic.
5 min read
Every topic
Practical articles on AI, automation and operational data: what works, what it needs and how to test it before you scale.
FAQs
Something else to ask?
Email your question with a little about your site and systems, and we will reply in plain language.
info@tashit.comNo. AMI data makes detection faster and more precise, but balance analytics can start with billing data and feeder, transformer or bulk-meter readings. The pilot also shows where additional metering would pay for itself.
Data stays in your environment or in a cloud region you approve. Access is role-based and logged, personal data is minimised in analysis datasets, and nothing is shared outside the project team. Data handling is agreed in writing before any data is transferred.
Yes. The balances and loss trends can feed the reports you prepare for the regulator, with the method documented. The figures you submit remain your responsibility; we make them traceable and repeatable.
Tell us about your site or process and the data you already have. We will reply with a diagnostic plan, the measures we would propose for a pilot and an honest view of feasibility.