Each topic below explains the evidence, the business problem it creates, how GLOBEIR solves it, the benefits to your organisation, and how your data stays private and secure.
1. RDSS Works Monitoring and Results Evidence
The Revamped Distribution Sector Scheme is results-linked: money flows only when a utility shows progress against loss and finance parameters. Under the scheme, projects worth Rs 2.83 lakh crore have been sanctioned, of which Rs 1.53 lakh crore is for distribution infrastructure such as conductor replacement, LT aerial bunched cable, transformer augmentation and agricultural feeder segregation [3]. Smart metering works alone cover 45 distribution utilities in 28 States and UTs [2].
The guidelines go beyond physical works. They provide for SCADA and Distribution Management Systems in about 100 larger towns and basic SCADA in 3,875 other statutory towns through district or circle-level control centres, require all feeders to be integrated with a National Feeder Monitoring System, and fund AI and ML tools that turn the monthly energy accounting reports into actionable management information on loss reduction, demand forecasting, asset management and renewable integration [1]. Each of these outputs is organised by feeder, transformer, town or circle, which means each one is a map layer as much as a table.
In practice, a single DISCOM may be running hundreds of work packages at once across many divisions, executed by several contractors, with a Project Management Agency appointed to support implementation [1]. Tracking whether a sanctioned reconductoring stretch, a new DT or an AB cable section was actually built where the DPR said, and whether it moved the loss figure on that feeder, is a spatial reconciliation problem.
Business problem
- Progress on RDSS works is often reported as counts and percentages per circle, with no reliable link to the feeder or DT the work was meant to fix. Managers cannot easily see whether money is going to the highest-loss parts of the network.
- Contractor claims, PMA verification and utility records are kept in separate spreadsheets, so disputes over what was done, where and when take time to resolve.
- Grant release depends on demonstrated loss and ACS-ARR performance [3], yet the evidence trail from a physical work to a measured result on a specific feeder is rarely assembled in one place.
Our solution
- A works monitoring layer on top of the network GIS: every sanctioned package is mapped to the feeders, DTs and line sections it covers, with planned versus executed geometry.
- Field verification through the My GLOBEIR app: geotagged, timestamped photographs and checklists for each completed work, captured by utility engineers or PMA staff and reviewed on a WebMap dashboard.
- Administration monitoring dashboards that roll up progress by division, circle and contractor, alongside the before-and-after loss trend on each treated feeder.
- Exportable evidence packs for review meetings, organised by feeder and work type.
Benefits
- One view of what was sanctioned, what was built and where, replacing manual reconciliation of contractor and utility records.
- Investment can be checked against the loss map, so works target the feeders that need them most. RDSS itself links fund release to AT&C and ACS-ARR performance (industry context) [3].
- Faster resolution of measurement and completion disputes because each claim carries a location, photograph and timestamp.
- A ready base for the AI and ML analytics that the scheme explicitly encourages on monthly energy accounting data (industry context) [1].
Privacy & data security
- Data involved: asset locations, works photographs, contractor and staff identities, and the GPS positions of field verifiers during inspections.
- Role-based access: contractors see only their own packages; PMA, division and headquarters users see what their role requires, with audit logs of every change.
- Critical infrastructure care: detailed network and substation layers are treated as sensitive operational data and kept on India-hosted or utility-controlled infrastructure, in line with the power sector's cyber security expectations [12].
2. Consumer Indexing and Network Asset Mapping
Consumer indexing ties every service connection to the pole, LT circuit, distribution transformer and feeder that supplies it. Asset mapping records the network itself: substations, HT and LT lines, poles, transformers, switches and meters, with their connectivity. Together they form the hierarchy that energy accounting depends on.
India has done this before, but not everywhere. R-APDRP made consumer indexing, GIS mapping and asset mapping of the network at and below 11 kV a part of baseline data preparation, but its project area was urban towns and cities with a population above 30,000, or above 10,000 in special category States [5]. Rural networks and smaller towns were largely outside that scope, and the urban data has aged as cities have grown. RDSS notes that IT measures under IPDS and R-APDRP, including GIS mapping of consumers and asset mapping, were carried out mainly in urban areas [1].
The pace of new connections makes the problem worse. The Electricity (Rights of Consumers) Rules, as amended in 2024, require a new connection within 3 days in metropolitan areas, 7 days in other municipal areas and 15 days in rural areas [15]. Connections released that quickly are rarely indexed to the correct DT at the same time unless indexing is built into the connection workflow.
Business problem
- Indexing done a decade ago no longer matches the ground. Feeder reconfigurations, new DTs and load shifting have moved consumers between transformers without the records following them.
- Rural and peri-urban networks, where many RDSS loss reduction works are planned, often have no reliable digital map at all.
- Time-bound connection rules push field offices to release connections quickly, and the network link is the field most often skipped [15].
- Survey campaigns run by multiple agencies produce inconsistent data that is hard to merge into the utility's GIS and billing systems.
Our solution
- Gap assessment of existing R-APDRP and IPDS layers against recent imagery and billing extracts, to decide where to update and where to map afresh.
- Network digitisation from high resolution imagery and field survey, following the utility's data model and topology rules, delivered through GIS mapping and cartography services.
- Door-to-door indexing with the My GLOBEIR app under mobile GIS workflows: offline capture, mandatory fields, photographs of meter and pole, and on-device checks against the mapped network.
- Digital micro mapping of dense areas where individual buildings, lanes and service cables must be resolved precisely.
- A maintenance workflow so that every new connection, shifting or disconnection updates the consumer-to-DT link at the time it is executed, not in the next survey cycle.
Benefits
- A consumer-to-DT-to-feeder hierarchy that energy accounting can trust, which R-APDRP treated as a prerequisite for measuring losses (industry context) [5].
- Extension of mapping to rural and smaller-town networks that earlier programmes did not cover (industry context) [5].
- Fewer false loss signals caused by mis-indexed consumers, so field effort goes where losses are real.
- A living map that stays current as connections are released within the regulated timelines [15].
Privacy & data security
- Data involved: consumer names, account and meter numbers, premises locations and photographs of meters and buildings. Location plus account identity is personal data under the DPDP Act [19].
- Minimum necessary capture: survey forms collect only what indexing needs; photographs are framed on the meter and pole, not on people or interiors.
- Encrypted capture: app data is encrypted on upload (TLS 1.2 or higher) and stored with AES-256 encryption at rest, with offline records synced only to the agreed server.
- India-only processing: detailed network and premises mapping is stored and processed in India, consistent with the DST geospatial guidelines [22].
3. AT&C Loss and Theft Hotspot Analysis
AT&C loss combines technical loss in wires and transformers with commercial loss from unmetered, under-billed, stolen or unpaid energy. National figures have improved, but the remaining loss sits in specific feeders, transformers and pockets. RDSS requires a feeder and DT level automated online energy accounting system and funds AI and ML tools to turn those accounts into monthly actionable reports [1].
The method is straightforward once indexing is sound. Input energy at the feeder and DT meter is compared with the sum of billed consumption beneath it. Large gaps flag a problem; spatial analysis then shows whether high-loss DTs cluster, whether they share a feeder or a type of area, and whether a gap is more likely a technical issue (long LT runs, overloaded or undersized conductors) or a commercial one. Research on smart meter data shows that consumption patterns themselves can reveal likely theft: a study combining principal component analysis with mean shift clustering reported encouraging results in detecting power theft among residential consumers [11].
Earlier programmes used independent verification of input energy and collection over at least three billing cycles to fix a baseline loss level [5]. The same discipline of verified baselines and repeat measurement is what lets a utility show that a loss reduction work actually delivered.
Business problem
- Loss figures are often reliable only at division or circle level, so action is spread thinly instead of concentrated on the DTs that account for most of the gap.
- Indexing errors, defective meters and real theft all look the same in a raw energy balance, and inspection teams waste visits separating them.
- Vigilance work routed by complaint or rota misses persistent hotspots and risks repeated visits to the same compliant consumers.
- Without a before-and-after comparison by feeder, it is hard to prove which interventions worked.
Our solution
- Monthly feeder and DT energy audit computed through the indexed hierarchy, with loss maps and ranked lists, built with geospatial data science workflows.
- Error triage that separates likely indexing errors, metering faults and suspected commercial loss before any field visit.
- Anomaly detection and hotspot analysis on meter data, tamper events and DT energy gaps using AI/ML geospatial models, with results shown as heat maps at DT and feeder level.
- Inspection planning that groups flagged connections into efficient routes and records each inspection outcome in the mobile app, feeding back into model training.
Benefits
- Loss reduction effort concentrated on the specific DTs and feeders that drive the gap, in line with the RDSS emphasis on DT level accounting (industry context) [1].
- Higher inspection productivity because likely indexing and metering faults are fixed first, without a vigilance visit.
- Data mining on smart meter consumption has shown encouraging results for residential theft detection in published research (industry example) [11].
- Evidence of impact by feeder, supporting the results-linked funding conditions of RDSS (industry context) [3].
Privacy & data security
- Asset-first analysis: models score meters, DTs and feeders on energy and consumption behaviour. No analysis uses personal or community attributes of consumers.
- Pseudonymised modelling: consumption series are analysed against meter identifiers; names and contact details are joined only for confirmed inspection lists, and only for authorised vigilance staff.
- Human decision: a model flag is a reason to inspect, not a finding. Final decisions remain with authorised utility officers under the utility's procedures.
- Purpose limitation: consumption data is used only for the agreed loss reduction purpose and never used to train models for other clients.
4. Smart Meter Rollout Planning and AMI Integration
RDSS has turned smart metering into the largest field programme in Indian distribution. Against 19.79 crore sanctioned consumer meters, 3.90 crore had been installed under the scheme by 31 December 2025, and 5.28 crore smart meters had been installed across all schemes [2]. The gap between sanction and installation is a logistics challenge spread over millions of premises.
The guidelines set clear priorities for the first phase: all Union Territories, electricity divisions in AMRUT cities with AT&C losses above 15 percent, industrial and commercial consumers, government offices at block level and above, and other high-loss divisions [1]. Areas without a communication network may take prepayment meters instead, and all DTs other than those feeding only agricultural consumers and those below 25 kVA are to carry communicable meters [1]. Every one of these rules is easier to apply on a map than in a list: AMRUT boundaries, loss levels by division, network coverage and DT capacity can all be overlaid.
Consumer trust matters as much as installation speed. Since 2024, a utility must install an additional check meter within five days of a consumer complaint that the meter reading does not match actual consumption, and use it for at least three months [15]. A REC and PFC feedback survey found that of 1,24,590 consumers who had downloaded the smart meter app, 54,321 were aware of its real-time consumption feature [2], which shows how much awareness work remains.
Business problem
- Installation agencies need daily targets by area, but sanctioned quantities are held as division totals with no premises-level plan.
- Meters installed without a verified location and network link create AMI data that cannot be used for DT energy accounting.
- Communication coverage gaps are discovered at installation time, causing repeat visits and stranded meters.
- Complaints about readings and prepaid balances cluster in particular areas, and utilities often cannot see the pattern quickly enough to respond with awareness drives or meter testing.
Our solution
- Rollout planning maps that combine indexed consumers, AMRUT and loss boundaries, DT capacity and communication coverage to sequence installation by priority category [1].
- Installer tracking through Live Tracking and the My GLOBEIR app: each installation is logged with GPS, old and new meter photographs and seal details, and validated against the GIS record.
- AMI-GIS reconciliation that flags meters reporting from the wrong DT, non-communicating meters and duplicate identifiers.
- Complaint and awareness dashboards on WebGIS that map check meter requests, recharge issues and app adoption by area, so awareness camps go where they are needed.
Benefits
- A premises-level plan for converting sanctioned quantities into installed meters; nationally, 3.90 crore of 19.79 crore sanctioned consumer meters were in place by December 2025 (industry context) [2].
- AMI data that is usable for DT and feeder accounting from the day of installation.
- Fewer repeat visits through upfront planning of communication gaps and prepayment meter areas [1].
- Faster, location-aware response to consumer complaints within regulated timelines [15].
Privacy & data security
- Data involved: interval consumption, prepaid balance and recharge records, tamper events, meter locations and installer movements. Fine-grained consumption can reveal household routines and is personal data under the DPDP Act [19].
- Aggregation by default: rollout and adoption dashboards show area or DT level summaries; individual consumption is visible only to authorised roles.
- Aligned with procurement terms: the RDSS Standard Bidding Document for smart metering already covers communication security, cloud security, incident management and DPDP compliance [2]; GLOBEIR follows the client's SBD obligations and security policies.
- Installer tracking is work-only: background tracking in the app is optional and opt-in, with a persistent notification while it runs.
5. Outage Management, Field Crews and Vegetation Along Lines
The Electricity (Rights of Consumers) Rules require distribution licensees to supply power 24x7 to all consumers, with lower hours permitted for some categories such as agriculture, and to put in place a mechanism, preferably with automated tools, for monitoring and restoring outages [14]. Automatic compensation is to be paid for performance standards that can be monitored remotely, including no supply beyond a specified duration and interruptions beyond specified limits [14]. Feeder metering under RDSS exists partly so that SAIFI and SAIDI can be calculated [1].
Restoration time is mostly a field problem: finding the fault, getting the right crew there and confirming the fix. A connected network GIS tells the control room which consumers sit downstream of a tripped switch; live crew positions tell it who can get there first.
Vegetation is a large and often unmapped cause of faults on overhead lines. The US Federal Energy Regulatory Commission's vegetation management review found that at many utilities, 80 to 90 percent of vegetation management funding and resources go to distribution rather than transmission, because distribution networks are so much longer [10]. It noted that most utilities did not have an inventory of the vegetation they manage, that trimming "cycles" across the industry ranged from 1 to more than 10 years, and that fixed cycles are not a sufficient way to ensure problems are addressed in time [10]. Imagery-based mapping of tree cover along corridors is the practical way to build that missing inventory.
Business problem
- Fault calls arrive at call centres and sub-division offices, but there is no quick way to see which consumers are affected and whether several calls point to the same fault.
- Crew dispatch relies on phone calls; supervisors do not know which crew is nearest or free.
- Compensation obligations tied to outage duration and frequency [14] make long restorations a financial as well as a service problem.
- Tree clearance follows fixed schedules or complaints rather than measured risk, so high-risk spans may fail in storms while low-risk spans are trimmed.
Our solution
- Outage dashboards that trace affected consumers from the faulted device through the network GIS, cluster incoming complaints by location and show restoration status, built under monitoring systems and WebGIS services.
- Crew tracking and dispatch with Live Tracking: nearest available crew, job assignment on the map and job closure with photographs and timestamps.
- Repeat-fault mapping that shows which feeder sections and DTs trip most often, to guide reconductoring, AB cable and other RDSS works.
- Vegetation risk mapping from high resolution satellite or drone imagery using remote sensing methods: canopy near conductors by span, ranked for clearance, with clearance work recorded and verified in the field app.
Benefits
- Faster identification of affected consumers and crew assignment, supporting the monitoring and restoration mechanism the Rules call for (industry context) [14].
- Evidence for feeder-wise reliability reporting based on the feeder metering that RDSS mandates (industry context) [1].
- A vegetation inventory where most utilities have none, and a move away from fixed cycles that the FERC review considered insufficient (industry example) [10].
- Clearance budgets directed to the spans that cause faults, where distribution can absorb 80 to 90 percent of vegetation spending (industry example) [10].
Privacy & data security
- Data involved: crew GPS trails, consumer complaint records with phone numbers and addresses, and imagery of corridors that may include private property.
- Opt-in work tracking: crew tracking runs only for work purposes, is optional and opt-in in the My GLOBEIR app, and shows a persistent notification while active.
- Complaint data minimisation: outage maps show affected areas and counts; complainant contact details stay in the call-centre system and are visible only to authorised staff.
- Imagery handling: high resolution corridor imagery and derived maps are stored and processed in India, as the DST guidelines require for finer-than-threshold data [22].
6. Rooftop Solar, Agricultural Feeders and DER Planning
Distributed generation is arriving on LT networks faster than many utilities planned for. PM Surya Ghar had more than 33 lakh rooftop systems installed by May 2026, adding over 12 GW, with a record 3.16 lakh installations in May 2026 alone and more than 65 lakh applications in the pipeline [16]. The Ministry expects to cross 75 lakh households by December 2026, and a Utility-Linked Aggregation model, under which about 30 lakh installations have been planned with utilities playing a key role, puts DISCOMs directly in the delivery chain [16].
Regulation has shifted the burden of network readiness to the utility. Since 2024, systems up to 10 kW no longer need a technical feasibility study; for larger systems the study must finish in 15 days or approval is deemed given; distribution strengthening needed for systems up to 5 kW is done at the DISCOM's own cost; and commissioning must happen within 15 days [15]. The utility therefore needs to know in advance which DTs are approaching their limits.
The same logic applies to agriculture. RDSS gives priority to segregating feeders that serve only agricultural load where they are to be solarised under PM-KUSUM, and once segregated those feeders are not to serve other consumers [1]. Identifying which feeders qualify, and where farm load and solar potential coincide, is a mapping exercise.
Business problem
- Rooftop applications are approved one by one, so the cumulative effect on a DT or LT circuit is invisible until voltage or reverse flow problems appear.
- With feasibility studies waived for small systems and strengthening costs falling on the DISCOM [15], unplanned DT upgrades can strain budgets.
- Utility-led aggregation needs target lists of suitable roofs and households by DT, which most utilities do not have [16].
- Agricultural feeder segregation and solarisation decisions depend on knowing which consumers sit on mixed feeders today [1].
Our solution
- Rooftop potential mapping from high resolution imagery: usable roof area, shading and orientation, summarised by DT and feeder through 3D mapping and remote sensing workflows.
- DER penetration dashboards that sum approved and commissioned rooftop capacity against DT rating and LT circuit length, flagging DTs likely to need strengthening before it becomes urgent.
- Aggregation planning maps for utility-led rooftop programmes, showing clusters of suitable roofs on DTs with headroom.
- Agricultural feeder analysis that maps farm connections, mixed feeders and candidate solarisation feeders using the indexed network.
Benefits
- Visibility of cumulative rooftop capacity per DT at a time when installations exceed three lakh a month (industry context) [16].
- Planned, budgeted strengthening for small systems that the DISCOM must now fund itself (industry context) [15].
- Better targeting for utility-led aggregation, where about 30 lakh installations have been planned across States (industry context) [16].
- A clear basis for prioritising agricultural feeder segregation under RDSS (industry context) [1].
Privacy & data security
- Data involved: roof geometry from imagery, applicant details from the national portal or utility systems, and generation and export readings from net meters.
- Analysis at DT level: rooftop potential and DER penetration are reported by DT and feeder; household-level outputs are limited to the utility's authorised programme teams.
- Imagery and 3D models in India: building-level imagery and models are finer-than-threshold geospatial data and are kept on servers in India, never passed to non-Indian entities [22] [23].
7. Other Networked Utilities: City Gas and Water
The same building blocks of a connected network map, indexed customers, field-captured assets and tracked crews apply to other utilities with buried or overhead networks.
City gas distribution. City gas coverage has expanded to 307 geographical areas, and domestic PNG connections reached about 1.57 crore by September 2025, with over 8,400 CNG stations [18]. Every new connection and every metre of pipe laid needs an as-built record, because a buried steel or polyethylene main that is not on the map is a safety risk for every later excavation.
Rural and urban water supply. Jal Jeevan Mission had provided tap water connections to 15.19 crore rural households (78.58 percent) by October 2024, and its monitoring system includes geo-tagging of assets created, third-party inspection before payment and sensor-based IoT measurement of supply [17]. As the focus shifts from building connections to sustaining service, utilities need the asset map to plan operations, leak repair and quality testing.
Business problem
- Gas and water networks are largely underground; as-built records captured on paper or by multiple contractors are incomplete and hard to merge.
- Rapid connection drives in both sectors [17] [18] add assets faster than records are updated.
- Field teams for meter reading, leak repair and emergency response are coordinated by phone, with limited visibility of where they are or what they found.
Our solution
Benefits
- A verified record of buried assets as networks expand across 307 city gas geographical areas (industry context) [18].
- Geo-tagged asset data of the kind Jal Jeevan Mission already uses for monitoring, extended into operations and maintenance (industry example) [17].
- One platform and field app across a multi-utility organisation or a municipal body that runs several networks.
Privacy & data security
- Data involved: customer addresses and connection details, pipeline routes and valve locations, and crew positions.
- Sensitive network layers: detailed pipeline and plant locations are shared only with authorised roles, with audit logs of access and export.
- Customer data minimisation: network analysis uses connection identifiers and aggregated counts; personal details remain in the utility's customer system.