87 types of energy system data

This course groups the GB energy estate into 87 distinct types of data. The number is not an industry standard: this taxonomy is the course’s own map, built by reading across every industry code (BSC, REC, SEC, UNC, DCUSA, CUSC, Grid Code), every regulatory instrument (Distribution Business Plans, RIIO Regulatory Instructions and Guidance, licence conditions), and every operational system (DCC DUIS, ElectraLink DTC, Elexon Data Integration Platform).

Each of the 87 data types has four defined attributes: its producer (who creates it), its consumer (who uses it), its governing instrument (which code or regulation mandates it), and its sensitivity classification (how openly it can be shared). The 87 types fall into 13 categories labelled A through M.

A. Consumption & Metering (7 types)

Half-hourly electricity consumption, non-half-hourly profiles, gas consumption, export metering, maximum demand, reactive power, and sub-metering. These are the foundational measurements that drive settlement, billing, and demand forecasting. Every supplier, network operator, and settlement body depends on Category A data. The non-half-hourly profile classes, the codes 1 to 8 that estimate a home’s daily consumption shape, are now legacy: under Market-wide Half-Hourly Settlement the Load Shaping Service and actual half-hourly readings replace profiling.

B. Smart Meter Infrastructure (6 types)

Meter metadata, communications status, DUIS service requests, IHD and CAD data, prepayment token management, and PKI certificate data. Category B covers the smart meter ecosystem itself - not the readings (which are Category A), but the infrastructure that makes readings possible.

C. Registration & Switching (6 types)

MPAN registration, MPRN registration, supplier switching events, market participant registration, address matching, and consent records. The Retail Energy Code governs most of Category C since the launch of the Central Switching Service in 2022.

D. Network Infrastructure (10 types)

Network topology, asset condition, fault records, GIS spatial data, LV monitoring, SCADA telemetry, protection settings, cable records, transformer data, and wayleave agreements. Category D is the largest single category because distribution networks comprise millions of individual assets, each generating operational data.

E. System Operations (8 types)

System frequency, inertia measurements, national demand outturn, power quality indices, fault level assessments, curtailment records, available capacity, and generator technical parameters. NESO publishes much of Category E on its Data Portal in near-real-time.

F. Wholesale Markets (8 types)

Balancing Mechanism data, settlement volumes, metered volumes, wholesale prices, interconnector flows, Capacity Market registers, Contracts for Difference data, and REGO certificates. Elexon manages the bulk of Category F through the BSC and the Elexon Data Integration Platform.

G. Ancillary Services (5 types)

Frequency response dispatch, reserve holdings, reactive power dispatch, black start contracts, and stability pathfinder data. These represent the services that keep the system stable second by second, and the data underpinning their procurement and delivery.

H. Flexibility & DER (6 types)

DNO flexibility procurement, Flexibility Measurement and Reporting (FMAR), EV charging data, battery storage dispatch, embedded generation registers, and heat pump monitoring. Category H is the fastest-growing - five years ago most of these data types did not exist at meaningful scale.

I. Gas System (6 types)

Linepack status, entry and exit flow data, gas quality and calorific value, shipper nominations, network shrinkage, and gas emergency data. The Uniform Network Code governs most of Category I, with National Gas Transmission as the primary data producer.

J. Low Carbon (6 types)

Carbon intensity data, CCUS monitoring, heat network performance, hydrogen blending trials, SF6 gas inventory, and network electrical losses. Category J is growing rapidly as decarbonisation commitments create new reporting requirements.

K. Planning & Forecasting (7 types)

Future Energy Scenarios, Strategic Spatial Energy Plans, Regional Energy System Plans, connections queue data, weather data for energy, Energy Performance Certificates, and ECO scheme data. Category K data shapes investment decisions worth billions of pounds across the next decade.

L. Offshore & Marine (3 types)

Offshore wind operational data, marine environmental monitoring, and offshore transmission owner (OFTO) data. Category L is the smallest but carries high individual value - a single offshore wind farm generates terabytes of SCADA data per year.

M. Regulatory & Compliance (9 types)

RIIO regulatory reporting, cybersecurity incident data, code modification proposals, supplier resilience indicators, complaints data, Priority Services Register, innovation project data, price cap calculation inputs, and theft detection data. Category M ensures the system operates within its legal and regulatory framework.

Not all 87 data types can be shared equally. The energy sector uses five sensitivity levels that determine who can access each type, under what conditions, and with what safeguards. Getting the classification right is critical: too restrictive and innovation is blocked; too open and people or infrastructure are put at risk.

Open

Open data is published freely, available to anyone without restriction. Examples include system frequency (E1), national demand outturn (E3), wholesale market prices (F4), carbon intensity data (J1), and Future Energy Scenarios (K1). Ofgem's Data Best Practice Guidance establishes its presumed-open principle: data should be open unless there is a clear reason otherwise.

Shared

Available to authorised parties under data sharing agreements. Examples include aggregated consumption profiles (shared between suppliers and DNOs), network topology data (D1, shared via the ENA Common Information Model), and connections queue data (K4, shared between DNOs and NESO). Access requires a legitimate purpose and usually a bilateral or multilateral agreement.

Restricted

Available only to parties with a demonstrated legitimate need, typically under strict contractual terms. Examples include detailed asset condition data (D2), protection relay settings (D7), and fault level assessments (E5). Disclosure could compromise network safety or give undue commercial advantage.

Personal

Subject to UK GDPR and the Data Protection Act 2018. Examples include half-hourly smart meter consumption data (A1) linked to a known address, Priority Services Register entries (M6), and switching history (C3). The ICO classifies granular consumption data as personal data because 48 daily readings from a known address can reveal occupancy patterns, routines, and lifestyle information about identifiable individuals.

Security

Critical national infrastructure data where disclosure could enable attacks on the energy system. Examples include SCADA configurations (D6), cybersecurity incident reports (M2), and detailed substation protection settings (D7). The 2015 Ukrainian power grid cyberattack - which cut electricity to 230,000 people by remotely opening circuit breakers - demonstrates why SCADA data must remain classified at the highest sensitivity level.

Every one of the 87 data types has a defined producer, consumer, and governing instrument. This triad matters because it determines who is responsible for data quality, who has legitimate access, and which legal framework applies when something goes wrong.

Take type A1 - half-hourly electricity consumption. The producer is the smart meter via the DCC network. The meter records a reading every 30 minutes and transmits it through the DCC WAN to the Data Communications Company, which routes it to authorised recipients. The consumers are suppliers (for billing), Elexon (for settlement), and DNOs (for network planning). The governing instruments are BSC Section S (which defines settlement metering requirements), the Smart Energy Code (which governs the DCC and meter communications), and the Market-wide Half-Hourly Settlement Direction (which mandates HH settlement for all meter points from 2027).

Or take type D6 - SCADA telemetry. The producer is the Remote Terminal Unit at each substation, sampling voltage, current, and switch positions every 2 to 10 seconds. The consumer is the DNO control room (for real-time network management) and NESO (for system-wide visibility). The governing instrument is the Distribution Code and the relevant DNO licence conditions, supplemented by NIS Regulations for cybersecurity.

“IEC 61968 defines the interfaces for applications involved in the management of a distribution network, providing an application integration standard for distribution management systems.”

IEC 61968 - Part 11

IEC 61968 is the governing standard for Category D data types. When a DNO's distribution management system exchanges network topology or asset data with another system, IEC 61968-compliant interfaces determine the format and semantics of that exchange. This is the standard underlying the CIM profiles used in the LTDS programme.

The triad is not merely academic. When a data dispute arises - a supplier claims a smart meter reading is wrong, or a DNO refuses to share network data - the governing instrument determines who is right. Understanding which code or regulation mandates each data type is the difference between making a persuasive argument and making an uninformed complaint.

The taxonomy earns its place only when it routes a real decision. Two datasets show how the same four questions send you down opposite paths.

Start with a single half-hourly import reading (type A1). Its category is A, Consumption and Metering. Its sensitivity is Personal the moment it is tied to a known address, because 48 daily values expose when a household is in. Its producer is the smart meter through the DCC; its consumers are the supplier for billing, Elexon for settlement, and the DNO for network planning. Its governing instruments are BSC Section S, the Smart Energy Code and the Market-wide Half-Hourly Settlement arrangements. Answer those four questions and the routing is decided for you: the reading becomes settlement data once Elexon allocates volumes from it, and its Personal classification means you cannot republish it openly, so anyone who wants it goes to the consent framework first.

Now take a Distribution Future Energy Scenarios (DFES) workbook. Its category is K, Planning and Forecasting. Its sensitivity is Open. Its producer is the DNO, usually working with Regen, not Ofgem and not NESO. Its consumers are NESO’s national forecasting, local authorities writing area energy plans, and connection applicants testing where the network has headroom. No single industry code mandates a DFES the way the BSC mandates a settlement read; each DNO produces it under its RIIO-ED2 planning obligations and publishes it under the Data Best Practice presumed-open principle. Because it is Open, you can pull the workbook straight into a connections study with no agreement to sign.

Same taxonomy, opposite handling. The A1 reading is locked behind consent; the DFES workbook is free to reuse. The classification is what tells you which path to take, and that is the test of whether a taxonomy is worth keeping: it has to change what you do next.