Energy Intelligence (ENERGYINT): Intelligence Discipline Guide

Energy is where physics, contracts and politics meet, and all three leave records. Energy intelligence reads the flows and the paperwork together, before the price move explains it for everyone else.

What Energy Intelligence is as a discipline

Energy intelligence is the analysis of energy production, transport, storage, trade and consumption as an intelligence problem: generation fleets and outages, grid load and interconnector flows, fields, refineries, pipelines, terminals and storage, tanker and cargo movement, market prices and contract structures, and the policy, tariff and sanctions regimes shaping all of it. It supports supply security assessment, critical infrastructure risk, counterparty and commodity analysis, and detection of evasion in energy trade.

Sub-methods include physical flow analysis from operator transparency data, outage and maintenance tracking, infrastructure mapping and construction monitoring through imagery, cargo and voyage analysis, refinery run and margin analysis, and policy and tariff monitoring. In the cycle it is a fusion discipline: it takes geospatial, imagery, maritime and market inputs and resolves them into a single reconciled picture of what is physically happening versus what is being reported.

Why it matters

Energy intelligence answers whether physical supply matches the commercial story: whether a plant is genuinely running, whether a pipeline is actually flowing and in which direction, and whether a cargo can be what its paperwork claims. It gives early warning of supply disruption affecting operations and costs, and it exposes the rerouting, blending and relabelling that sanctions evasion in energy trade depends on.

What analysts actually look for

These are the concrete, observable signals that carry weight in this area of work:

Interconnector and pipeline flow data showing direction and volume changes before any public announcement.
Planned and unplanned generation outage notices, and the pattern of repeatedly extended return-to-service dates.
Gas and oil storage levels measured against seasonal norms, indicating stress or deliberate stockbuilding.
Thermal and flaring signatures from satellite showing whether a facility is actually operating at all.
Construction progress at terminals, refineries and substations visible across repeat imagery passes.
Tanker voyage patterns, transfers at sea and draught changes inconsistent with the declared cargo.
Price spreads and basis differentials implying a physical constraint no operator has yet announced.
Regulatory filings, tariffs and capacity auction results revealing who holds contracted capacity and for how long.

Where the data comes from

Authoritative and openly available collection points. Always confirm licensing and terms before operational or commercial use:

ENTSO-E Transparency Platform — European electricity generation, load, outage and cross-border flow data at high frequency
ENTSOG and AGSI platforms — European gas transmission flows and near real-time storage levels by facility and country
US Energy Information Administration — Open production, stocks, capacity, price and trade data with long consistent historic series
JODI Oil and Gas World Database — Country-reported production, refining, stocks and trade figures for cross-checking national claims
Global Energy Monitor trackers — Open asset-level datasets on plants, pipelines, terminals and their development status
NASA VIIRS Nightfire and FIRMS — Satellite thermal detections identifying flaring and active industrial heat sources
IAEA PRIS — Reactor-level status, capacity and operating history for civil nuclear power worldwide
OPEC Monthly Oil Market Report — Production, demand and balance estimates with the secondary source detail behind them

A working method

A repeatable sequence beats ad-hoc searching. This is a practical starting workflow:

Define the asset or corridor — State whether the question concerns a facility, a route, a market or a counterparty, since each needs different data at different cadence.
Build the physical baseline — Map assets, capacities, routes and ownership for the area in scope, recording nameplate and historically achieved throughput separately.
Ingest transparency data — Pull operator and regulator publications on a schedule, retaining revisions so later restatements are visible rather than silent.
Verify with imagery — Confirm operational status using thermal signatures, flaring, vessel presence and visible activity at the site.
Reconcile physical and commercial — Compare measured flows against trade reporting, contracts and price signals, and investigate where they disagree.
Test hypotheses — Assess whether an anomaly is maintenance, weather, demand, evasion or disruption, and identify what would distinguish them.
Report with data caveats — Publish stating the reporting lag, revision policy and coverage gaps, because energy data is routinely restated after publication.

How this connects across the intelligence taxonomy

Intelligence work does not respect neat boundaries. The mission domain you are working, the disciplines you practise, and the data points you pivot on are one connected system. These are the direct relationships for this entry — every link is also a tag, so you can follow any thread across the whole library.

Applied in these mission domains

Operates on these data points

Facility / Site — A physical installation — plant, base, port, data centre — with a fixed footprint and function.
Location / Coordinates — A geographic point, place, or region — the basis of GEOINT analysis.
IP Address — Internet Protocol address identifying a device or server on a network.
CVE / Vulnerability — Common Vulnerabilities and Exposures identifier for a known flaw.
ASN — Autonomous System Number identifying a network operator on the internet.
Event / Incident — A discrete real-world occurrence — protest, strike, breach, seizure — with time, place, and actors.

Inside the platform: where Energy Intelligence lives

The Quantus platform is 204 pages behind a 147-item sidebar organised into six working groups: Command (24 items), Dashboards (15), Threat Theaters (14), Intelligence Domains (15), Investigate (34), and Administration (45). This entry is not a page in isolation — it is a thread running through several of them.

The modules that matter most here:

discipline.php?d=ENERGYINT — Discipline hub
source-catalog.php?disc=ENERGYINT — Source catalogue filtered to this discipline
ip-profile.php — IP Address profile
detection-rules.php — CVE / Vulnerability profile
asn-profile.php — ASN profile
search.php — Advanced search, filter and pivot
correlate.php — Correlation graph
cases.php — Case management

Each dashboard is local-first: it renders from the platform’s own database rather than depending on a live third-party call, so it still works when an upstream API is unreachable or rate-limited. Heavy aggregates are cached with a hard query time cap and degrade to the last good value instead of hanging the page.

Automation, playbooks and AI skills

Analysis that only happens when someone remembers to run it is not a capability. The platform ships a 30-step automation pipeline (cron.php) that collects, ingests, resolves, enriches, correlates and scores on a schedule — 25 seeders, 11 resolvers and 7 enrichment runners, all idempotent and cursor-based so a run can be interrupted and resumed without duplicating or losing work.

AI skills that apply

The 16 one-click operations in ai-skills.php are deterministic jobs, not free-text generation. The ones that matter here:

Threat Hunt
Auto-Collect Feeds
Enrichment → Local
Summarise (Copilot)
Generate Report

Alerting closes the loop: rules in alerts.php fire on new indicators matching a saved query, so a first sighting in this area raises a notification rather than waiting to be noticed at the next review.

Feeds, data sources and the API

The collection layer runs a feed registry of free, machine-readable sources — bulk blocklists and trackers (Maltrail, IPsum, FireHOL, the full abuse.ch corpora, phishing databases, Emerging Threats, Spamhaus, DigitalSide, ThreatView), authoritative government feeds (CISA KEV, OFAC, UN and EU sanctions lists), and reference datasets (RIR allocations, ip-to-ASN and geolocation tables, MITRE ATT&CK, EPSS). collect.php pulls them server-side on a schedule; feeds.php and source-catalog.php show what is registered, what it covers and when it last ran.

Anything the platform holds is reachable programmatically. The REST API in api.php exposes 11 endpoints — status, stats, search, lookup, recent, export, bulk_check, top_threats, by_category, categories, check — and export.php streams 18 formats in bounded chunks, so a million-row export neither exhausts memory nor times out:

STIX 2.1, MISP, OpenIOC 1.1, CEF (ArcSight), LEEF 2.0 (QRadar), Zeek/Bro intel, Snort/Suricata rules, Palo Alto EDL, BIND RPZ, hosts blackhole, iptables, CSV, JSON, NDJSON/JSONL, XML.

That covers the CTI standards (STIX 2.1, MISP, OpenIOC), SIEM ingestion (CEF, LEEF, Zeek), detection engines (Snort/Suricata), and direct enforcement (Palo Alto EDL, BIND RPZ, hosts, iptables) — so intelligence developed here can be actioned in the tools you already run, without a manual reformatting step. A TAXII 2.1 server and a MISP/RSS feed are also served for pull-based sharing.

Use cases

Three ways this entry earns its keep in day-to-day work:

Triage under time pressure. An artifact or report lands and you need a defensible read in minutes, not days. Define the asset or corridor is the first move; the platform pre-computes the enrichment so the analyst spends the time on judgement rather than lookups.
Building the picture. A single indicator is rarely the story. Ingest transparency data turns one artifact into a network — shared infrastructure, repeated selectors, the same operator behind different names — via the correlation graph and the cross-entity link engine.
Producing something actionable. Analysis that ends in a document nobody can use is wasted. Report with data caveats feeds the case file, the detection rule, the block list or the referral — with sourcing attached so the recipient can verify it.

Case management (cases.php), watchlists, saved searches and scheduled reports mean the work persists between sessions and survives an analyst leaving the team.

How each sector uses Energy Intelligence

The same entry is worked very differently depending on who you are, what authority you hold, and what you are ultimately producing. A military analyst is supporting a commander’s decision; a journalist is meeting a publication standard; an NGO caseworker is protecting a person. The underlying artifacts are shared — the constraints, outputs and thresholds are not.

🎖 Military and defence

Energy analysis supports assessment of an adversary's or partner's war-sustaining capacity, logistics vulnerability and civil resilience. Analysts track refinery throughput, fuel distribution, grid stability, generation outages and interconnector dependence, and assess how disruption would propagate through civil and military systems. It supports infrastructure protection planning, understanding of an operating environment's power reliability, and assessment of second-order humanitarian consequences of any effect on energy infrastructure. Constraints are serious: energy assets are civilian objects and critical infrastructure with legal protections, detailed vulnerability analysis of specific facilities is sensitive and often restricted, and consequence assessment for civilian populations is a legal requirement rather than a courtesy.

🕵 National intelligence

Agencies use energy intelligence for supply security assessment, sanctions enforcement support, and understanding of the economic base underpinning a state's behaviour. It is a fusion discipline: transparency platform data, imagery of facilities and storage, vessel tracking, trade statistics and market pricing are reconciled to show what is physically happening versus what is being reported. Requirements often concern discrepancy, since divergence between declared and observable flows is where evasion and misreporting appear. Handling includes market-sensitive information constraints and careful separation of observed physical facts from inferred commercial arrangements.

👮 Law enforcement

Investigators engage with energy through sanctions evasion, fraud, smuggling, market manipulation and environmental offences. Evidential standards require documentary trails: bills of lading, cargo manifests, inspection certificates, customs declarations and payment records, obtained through production orders and MLAT rather than inferred from tracking data. Vessel transponder data supports a case but does not prove a transfer occurred; corroboration through imagery, port records and testimony is needed. Market abuse investigations require establishing what information was non-public and when, which turns on documented timing rather than plausible inference.

🔍 Private investigation and corporate security

Corporate teams use energy intelligence for counterparty due diligence, supply security assessment, commodity trade compliance and project risk. Legitimate work uses regulatory transparency data, published filings, licensed market data and open imagery. A private actor must respect market abuse and insider dealing rules, which apply to non-public operational information about facilities and outages, must observe the reuse terms attached to regulatory transparency data, and must not conduct or commission intrusive collection against critical infrastructure, which is a criminal matter and a national security concern.

📰 Journalism and OSINT media

Reporting on energy requires competence with units, timeframes and the difference between capacity, generation and consumption, which is where most published errors originate. Verification means going to operator transparency data and official statistics rather than relying on company statements, and understanding maintenance schedules before characterising an outage as an incident. Right of reply is important because energy allegations move markets and share prices. Publication ethics include care with detailed infrastructure vulnerability information, which can be operationally useful to hostile actors and offers limited public benefit at that granularity.

🌍 NGO, humanitarian and human rights

Humanitarian and development organisations use energy intelligence for understanding access, resilience and the humanitarian consequences of disruption: hospitals without power, water pumping dependent on fuel, cold chain failure and heating in winter. Environmental organisations track emissions, flaring, methane leakage and project development. Do-no-harm requires care that infrastructure analysis does not provide targeting-useful detail in conflict settings. Documentation for accountability includes recording attacks on energy infrastructure and their civilian consequences, which requires the same preservation standards as any accountability work.

🎓 University and research

Researchers study energy systems, market behaviour, transition pathways and the physical infrastructure record. Reproducibility requires citing dataset versions and vintages, because transparency platform data is revised retrospectively and modelled series change between releases. Ethics review is rarely engaged unless individuals are involved, but licence terms on regulatory data and commercial market data constrain redistribution significantly. Publish code, unit conversions and reconciliation methods explicitly, since much apparent disagreement in the literature stems from unstated differences in boundaries, units and reporting conventions.

Playbook: working Energy Intelligence end to end

A repeatable sequence, from the moment the requirement lands to the moment a product is delivered and the case is closed out. Each phase states what you are trying to establish, not merely what to click — the point is a defensible chain of reasoning, not a checklist.

Phase 1 — Frame the energy question

State precisely what must be established: physical availability of a fuel, resilience of a grid, throughput at a facility, the real destination of a cargo, or exposure of a counterparty. Attach the decision and the required precision, because a directional answer and a quantified one demand entirely different work. A good output is a question naming the commodity, the system boundary, the period and the accuracy needed. Stop when the boundary is defined, since energy figures are meaningless without one.

Phase 2 — Fix units and boundaries

Define the units and conversions before touching data: energy versus power, gross versus net calorific value, barrels versus tonnes with the density assumption stated, cubic metres at which temperature and pressure. Define the system boundary: production, exports, domestic consumption, or the whole chain including losses. Most published disagreement in this field is unit and boundary confusion rather than substantive dispute. A good output is a conversion and boundary note. Stop when a second analyst could reproduce your figures from it.

Phase 3 — Map the physical system

Build the infrastructure picture: fields, mines, processing plants, refineries with configuration and capacity, pipelines with direction and capacity, storage with type and working volume, terminals, generation fleets by technology and age, transmission and interconnectors. Record ownership and operator for each asset. Use imagery to confirm existence and status rather than trusting lists. A good output is an asset inventory with capacities and operators sourced. Stop when the assets material to the question are inventoried and located.

Phase 4 — Establish the baseline

Characterise normal operation: seasonal demand shape, typical maintenance windows, historic outage frequency, usual import and export patterns, and normal storage cycles. Maintenance is scheduled and published, so an outage during a known window is routine rather than an event. Without this, every planned shutdown reads as a disruption. A good output is a seasonal baseline with the maintenance calendar. Stop when you can say what an ordinary month looks like for this system.

Phase 5 — Pull operator transparency data

Collect the published physical data that system operators are obliged to release: generation by unit and fuel, load, cross-border flows, outage notifications, gas flows and storage levels. This is the closest thing to ground truth available openly, and it is under-used. Record the publication vintage, because these series are revised retrospectively. A good output is a time series with source and version recorded. Stop when the physical series cover the period and assets in question.

Phase 6 — Track movement and cargoes

Where the question involves trade, follow vessel movement, draught changes, port calls and ship-to-ship activity, alongside published customs and trade statistics. Treat transponder data as one input, since it can be switched off or falsified, and corroborate gaps with imagery and port records. Reconcile declared cargoes against physical capacity. A good output is a voyage picture with the evidence for each leg. Stop before asserting a transfer occurred on transponder gaps alone.

Phase 7 — Confirm with imagery

Use satellite imagery to verify physical status: floating roof tank heights indicating storage levels, flaring and thermal signatures, vessel presence at berths, construction progress, and damage assessment. Radar supports observation under cloud and at night. Establish a baseline before reading change, and describe measurements before interpreting them. A good output is an imagery-derived observation with dates and stated accuracy. Stop when the imagery corroborates or contradicts the reported picture.

Phase 8 — Reconcile the picture

Compare declared figures against physical observation and against mirror data from counterparties: production reported against export statistics received by importers, refinery runs against product output, generation against fuel consumption. The reconciliation gap is the analytic product. Identify whether a discrepancy reflects misreporting, definitional difference, storage change or genuine concealment. A good output is a reconciliation table with gaps quantified and explained. Stop when each significant gap has a candidate explanation and a test.

Phase 9 — Assess market and contract context

Layer in prices, benchmarks, spreads and contract structures: whether flows follow economics, whether discounts indicate constrained buyers, whether re-invoicing prices are detached from published benchmarks. Be rigorous about market abuse rules; non-public operational information must not be traded on or selectively disclosed. A good output is a commercial assessment grounded in published benchmarks. Stop before inferring a contract's terms from price behaviour alone.

Phase 10 — Evaluate resilience and consequence

Model what disruption would mean: which loads are lost, for how long, what redundancy exists, how quickly storage depletes, and what the second-order consequences are for water, health facilities, cold chain and heating. Consequence analysis for civilian populations is an obligation in conflict contexts, not an optional extra. A good output is a consequence assessment with quantified durations and affected functions. Stop when the humanitarian dimension is addressed alongside the technical one.

Phase 11 — Apply sensitivity and legal review

Review the product for information that is market sensitive, licence restricted or operationally useful to a hostile actor. Detailed vulnerability analysis of specific facilities is restricted in many jurisdictions and carries real-world risk. Check the reuse terms of every regulatory dataset relied on before redistribution. A good output is a reviewed product with restricted material removed or handled under controlled distribution. Stop when the residual sensitivity is proportionate to the purpose.

Phase 12 — Deliver and maintain

Publish with units, boundaries, data vintages and reconciliation gaps stated plainly, and distinguish observed physical facts from inferred commercial conclusions. Then maintain: energy pictures decay quickly through outages, maintenance, sanctions changes and price moves, so set a refresh cadence and trigger off-cycle updates on major events. A good output is a maintained assessment with a visible as-of date. Stop maintaining only when the requirement ends, marking superseded versions clearly.

The platform ships this as a step-checked workflow in playbooks.php, so progress is recorded against a case rather than held in someone’s head.

Source register: what to collect from, and how

Sources are listed with their access model so you can plan around cost and licensing before you build a dependency on them. Open means no account required; registration means a free account or API key; licensed means paid or institutional access. Always confirm current terms — licensing changes, and a source that was free for research may not be free for commercial or evidential use.

Source	Access	What it gives you	How it is used here
ENTSO-E Transparency Platform	Registration	European electricity data including generation by unit and fuel, load, cross-border flows, outages and capacity.	Closest available ground truth on European power system operation, published under regulatory obligation with defined reuse terms.
ENTSOG and gas transparency platforms	Open	European gas transmission data including flows at interconnection points, capacity and nomination information.	Shows physical gas movement between systems, revealing rerouting and dependence that contract data conceals.
Gas Infrastructure Europe AGSI storage data	Open	Daily gas storage inventory, injection and withdrawal data by facility and country across Europe.	Tracks storage depletion and refill against seasonal norms, which is the key resilience indicator for gas supply.
US Energy Information Administration	Open	Comprehensive US and international energy statistics covering production, refining, stocks, trade, prices and generation.	Authoritative long time series and definitions, and the reference for refinery, stock and production reporting conventions.
Joint Organisations Data Initiative	Open	Country-reported monthly oil and gas production, demand, trade and stock data submitted by participating administrations.	Comparable cross-country reporting used for mirror analysis and for detecting divergence between declared and observed flows.
International Energy Agency	Registration	Statistics, market reports and policy analysis covering oil, gas, electricity, renewables and energy security.	Authoritative market context and methodological reference for balances, definitions and security-of-supply analysis.
Global Energy Monitor	Open	Open trackers of coal, gas, oil, pipeline, LNG terminal, wind, solar and steel infrastructure with status and location.	Asset-level inventory with coordinates and development status, forming the backbone of an infrastructure picture.
IAEA Power Reactor Information System	Open	Official database of nuclear power reactors worldwide including status, capacity, operational history and outages.	Authoritative record of nuclear generation capability and availability, needed for any electricity balance assessment.
NASA FIRMS thermal anomaly data	Open	Active fire and thermal anomaly detections with location, timestamp and confidence from satellite instruments.	Detects flaring, refinery fires and thermal activity at facilities, giving timestamped evidence of operational status.
Copernicus Data Space Ecosystem	Registration	Sentinel optical and radar imagery with processing tools, at no cost and with open licensing.	Verifies storage levels, construction progress, damage and vessel presence at terminals independent of reported data.
UN Comtrade	Open	Reported bilateral merchandise trade statistics by commodity and partner, including mirror data from both sides.	Mirror analysis of declared energy trade, exposing rerouting, re-invoicing and unreported flows through third countries.
OPEC publications	Open	Monthly market reports, production data and member country statistics with secondary source production estimates.	Reference on production quotas and reported output, and a comparator against independently estimated production.
IRENA statistics	Open	Global renewable energy capacity, generation and cost data by country and technology with annual updates.	Baseline for renewable capacity and generation when assessing system composition and transition trajectories.
IMO GISIS and vessel registries	Registration	Official ship particulars, ownership, management and history of name, flag and owner changes.	Establishes tanker identity and ownership chains behind cargo movements, particularly where sanctions exposure is in question.
National regulators and system operators	Open	Grid operator publications on system status, outages, constraint payments, balancing and network development plans.	Country-level operational detail and forward network plans that pan-European platforms aggregate away.

Prefer sources that publish a methodology and a revision history. A dataset that changes silently is a liability in any product that has to survive challenge.

Tooling

Tools commonly used against Energy Intelligence. None of these replace judgement, and each carries its own failure modes — know what a tool infers versus what it observes.

Transparency platform data clients — Retrieve structured operator data at scale for time series analysis. Limitation: series are revised retrospectively, so an analysis must record the data vintage or it cannot be reproduced.
Vessel tracking and maritime analytics — Combine transponder history, draught, port calls and ownership to infer cargo movement. Limitation: transponders can be switched off or falsified, and gaps require independent corroboration rather than assumption.
Satellite imagery analysis for storage and flaring — Measures floating roof tank heights and detects thermal signatures at facilities. Limitation: tank measurement requires favourable geometry and a baseline, and thermal detections do not distinguish flaring from fire.
Thermal anomaly feeds — Provide timestamped detections of high-temperature events at industrial sites. Limitation: resolution and revisit limit detection of small flares, and detections carry confidence values that are routinely ignored.
Energy balance and unit conversion frameworks — Enforce consistent units, calorific values and system boundaries across mixed sources. Limitation: conversion assumptions such as crude density vary by grade and materially change tonne-barrel figures.
Market data terminals and price assessment services — Supply benchmark prices, spreads and forward curves for commodity analysis. Limitation: licensed and expensive, with redistribution restrictions that follow into any derived product.
Geographic information systems for infrastructure — Locate assets, model pipeline routes and assess proximity and exposure. Limitation: asset coordinates in open datasets are frequently approximate, and precision claims should be verified against imagery.
Trade statistics reconciliation tooling — Compares reported exports against partner-reported imports to expose divergence. Limitation: definitional differences, timing lags and transhipment produce large legitimate gaps that must be modelled before residuals mean anything.

AI skills and automation in detail

These are deterministic jobs with defined inputs and outputs, not open-ended prompting. Each is idempotent and cursor-based: interrupt one and it resumes where it stopped rather than duplicating work or losing progress.

Threat Hunt — Runs saved hypotheses against the corpus and surfaces what matches, with the query preserved as a versioned artifact.
Auto-Collect Feeds — Pulls the registered feed set server-side on a schedule, recording per-feed status so a silently dead feed is visible.
Enrichment → Local — Materialises enrichment into the local store so dashboards render from your own database instead of a live third-party call.
Summarise (Copilot) — Produces a narrative summary beside the underlying records. It explains; it never creates indicators or assigns attribution.
Generate Report — Assembles a sourced product from the current case or query, with provenance attached to each element.

A note on the boundary: the only skill that involves a language model is Summarise (Copilot), and it writes prose about records that already exist. Nothing else on this list involves generation of any kind. No indicator, relationship or attribution in the platform originates from a model. See the full skill list.

Tradecraft notes

The distinctions that separate a competent analyst from a fast one:

Fix units, calorific basis and system boundary before touching data. Most published disagreement in energy analysis is unit and boundary confusion rather than substantive dispute, and it is entirely avoidable.
Capacity, generation and consumption are three different quantities and are constantly conflated. A country with large installed capacity and low availability has a supply problem that headline capacity figures actively conceal.
Check the maintenance calendar before calling an outage an incident. Scheduled shutdowns are published in advance, and misreading one as a disruption is the most common error in energy monitoring.
Transparency platform data is revised retrospectively. Record the vintage of every series you use, because an analysis run today and rerun next month will produce different numbers with no error on either side.
Transponder gaps are not evidence of a transfer. They are evidence that transmission stopped, which has many explanations, and asserting ship-to-ship activity without imagery or documentary corroboration is a routine overreach.
The reconciliation gap between declared and observed flows is the analytic product, not a nuisance. Quantify it, then work through definitional, timing, storage and concealment explanations in that order.
Physical observation outranks reporting. Storage levels measured from imagery, flaring detected thermally and flows published by system operators are harder to misstate than production figures reported by an interested party.
Consequence analysis is part of the job in conflict contexts. Energy infrastructure is civilian, and an assessment of disruption that omits hospitals, water pumping, cold chain and heating is incomplete as analysis, not merely as ethics.
Treat detailed facility vulnerability analysis as restricted by default. The public benefit of that granularity is usually small and the operational utility to a hostile actor is not, so publish at the level the argument requires and no further.

Measuring whether it is working

Capability claims should be falsifiable. These are the measures that show whether work on Energy Intelligence is producing anything, and they are worth baselining before you change process or tooling.

Proportion of quantitative findings published with units, calorific basis, system boundary and data vintage stated explicitly.
Reconciliation accuracy: size of the unexplained gap between declared and independently observed flows, trended per commodity and route.
Rate at which reported outages were correctly classified as scheduled maintenance versus unplanned events, checked against published calendars.
Share of cargo movement conclusions supported by corroboration beyond transponder data, such as imagery, port records or documentary evidence.
Currency of the asset inventory, measured as the proportion of material assets verified by imagery or filings within the stated refresh cadence.
Number of assessments including a quantified civilian consequence analysis where energy disruption in a populated or conflict area is in scope.
Licence compliance rate, measured as the share of redistributed datasets whose reuse terms were checked and recorded before publication.

Beware of measuring volume alone. Indicator counts and report counts rise easily and say little; time-to-attribution, proportion of findings that survive review, and how often a product changed a decision say a great deal.

Common pitfalls

Taking nameplate capacity as available capacity, ignoring degradation, ambient conditions and maintenance.
Ignoring reporting lags and revisions, so a provisional figure is treated as a confirmed change.
Mistaking routine seasonal maintenance patterns for unplanned disruption, deliberate withholding or the effect of sanctions.
Assuming flow data covers all routes, when reporting obligations differ sharply by operator and jurisdiction.
Reading a single thermal detection as plant operation without a seasonal and historic baseline.
Conflating owner, operator and flag or registered charterer, which are frequently three different parties.

Legal and ethical considerations

Much energy transparency data is published under regulatory obligation with specific reuse terms, so check licences before redistributing or building products on it. Energy assets are critical national infrastructure, and detailed vulnerability analysis or precise imagery of sensitive facilities may be restricted and can create real-world risk. Market data use is bounded by market abuse and insider dealing rules, so treat any non-public operational information with care and keep sanctions-related conclusions documented and evidence-based.

Data integrity: no fabrication, no drift, no hallucination

Intelligence that cannot be traced back to a source is not intelligence, it is assertion. Everything in this entry — and everything in the platform behind it — is built on a small number of non-negotiable rules.

Provenance on every record

Every indicator carries the source that supplied it, a first-seen and last-seen timestamp, and a sighting count. Where several feeds report the same artifact, each contribution is recorded separately rather than collapsed, so you can see whether a finding rests on one source or twelve. Source attribution travels with the data into every export, so a recipient can audit a claim without asking you for the working.

Nothing is invented to fill a gap

If the platform has no data for Energy Intelligence, it says so. Empty is displayed as empty — never padded with plausible-looking placeholder values, sample records or illustrative examples that a reader might mistake for observations. A dashboard with no rows is a true statement about collection coverage, and it is treated as a gap to close, not a blemish to hide.

Scoring is deterministic and reproducible

Threat scores, reputation grades and risk tiers are computed from stated inputs with fixed weights, not estimated. The same inputs always produce the same output, and the formula is visible rather than a black box. Aggregates are cached with an explicit time-to-live so a figure on screen is never silently stale — and when a heavy query exceeds its time budget the platform serves the last known-good value and labels it, rather than inventing a fresh number or hanging.

Where AI is used, and where it is not

Language models summarise and explain. They do not create indicators, assign attribution or manufacture relationships. No IP address, wallet, hash or identity in the platform originates from a model — every one is ingested from a named feed, resolved from a reference dataset, or entered by an analyst with a source recorded. Copilot output is presented as narrative alongside the underlying records, never in place of them, so a reader can always check the summary against the evidence.

Guarding against drift

Enrichment is additive and timestamped rather than overwriting. Reference data — sanctions lists, allocations, taxonomies — is re-synchronised from the authority on a schedule instead of being edited in place, so local copies cannot quietly diverge from the source of truth. Attribution is recorded with a confidence level and the reporting it rests on, and inferred relationships are labelled as inferred. When a source retracts or corrects, the correction propagates rather than leaving a stale assertion behind.

What this means for you

You can put a finding from this platform in front of a regulator, a court, a board or a partner agency and show where each element came from. That is the standard the tooling is built to — because in this work, being confidently wrong is more damaging than being usefully uncertain.

By the numbers

The taxonomy this entry belongs to is not a marketing list — it is the actual structure of the platform: 52 mission domains, 52 intelligence disciplines and 65 data points, each with a live dashboard behind it. Supporting that: 18 indicator types, 14 playbooks, 16 AI skills, 18 export formats and a 30-step automated pipeline.

This particular entry connects directly to 6 data points, 2 mission domains — every one of them a tag you can follow, and a dashboard you can open.

Questions analysts actually ask

Why do energy figures from different sources disagree so much?

Usually because they measure different things. Capacity, availability, generation and consumption are distinct; gross and net calorific value differ by several percent; barrels and tonnes convert at a rate that depends on crude density; gas volumes depend on the temperature and pressure convention; and system boundaries vary in whether they include losses, own use, bunkers or transit. Add retrospective revision and different reporting periods and large apparent discrepancies arise with no error at all. Establish units, basis, boundary and vintage for each source before comparing, and state them in the product so a reader can reconcile independently.

Can satellite imagery really measure oil storage?

For external floating roof tanks, yes, within useful accuracy. The roof sits on the liquid, so its height relative to the tank wall indicates fill level, and shadow measurement against solar geometry allows estimation across a tank farm. Accuracy depends on resolution, collection geometry, knowledge of tank dimensions, and a baseline established across multiple dates. It does not work for fixed roof or internal floating roof tanks, or for underground and cavern storage, which is a substantial share of capacity in some countries. Report estimates with uncertainty and state which tanks were and were not measurable.

What does an AIS gap actually tell you?

That transmission stopped, and nothing more on its own. Legitimate causes include equipment failure, poor satellite coverage in certain regions, terrestrial receiver gaps and congestion. Deliberate disabling is common in sanctioned trades, but so is spoofing, where a vessel transmits a false position while physically elsewhere. Treat a gap as a question rather than an answer: check imagery over the plausible area during the gap, compare draught before and after for evidence of cargo change, check port call records, and look for documentary evidence. Assertions of ship-to-ship transfer built on gaps alone do not survive scrutiny.

Is it lawful to trade on this kind of analysis?

Analysis built from public and licensed data is generally lawful, and this is the basis of the commodity research industry. The line is inside information: non-public, precise information about a facility, outage or company that a reasonable investor would use, which cannot be traded on or selectively disclosed. The risk in energy work is real because operational information sometimes reaches analysts before publication, for example an unannounced outage observed thermally versus one disclosed by an insider. Observation of a public signal is not inside information; receipt of non-public operational information from a person with duties is. Document how each input was obtained.

How do you detect sanctions evasion in energy trade?

Through reconciliation and pattern rather than a single indicator. Look for divergence between exporter-declared volumes and importer-reported receipts, cargoes routed through non-implementing jurisdictions with re-invoicing at prices detached from published benchmarks, vessels changing name, flag or registered owner close to a designation date, ageing tankers acquired by newly formed owners, transponder behaviour changes, and ship-to-ship activity in known transfer areas. Corroborate with imagery and ownership records from official ship registers. Each indicator alone is weak; the combination, evidenced item by item, is what supports a conclusion a client or authority can act on.

What should not be published about energy infrastructure?

Detail whose main utility is to someone who wants to attack it. Specific vulnerability analysis, control system detail, precise layouts of critical nodes, single points of failure identified at component level, and security arrangements all fall in this category. In several jurisdictions such information is legally restricted as critical national infrastructure material. Publish at the level of the analytic argument: that a system depends heavily on two interconnectors is a legitimate public finding; a detailed assessment of how to disable them is not. Where the analysis is needed by operators or authorities, distribute it under control rather than openly.

How quickly does an energy assessment go stale?

Faster than most reference products. Outages, maintenance, weather, sanctions changes, price moves and infrastructure commissioning all shift the picture within weeks, and storage positions change daily during a heating season. Set the refresh cadence from the volatility of the binding constraint rather than from convenience, put the as-of date on the face of the product, and trigger off-cycle updates on major events such as a designation, a facility incident or an unplanned outage at a critical asset. Mark superseded versions clearly, because a stale energy assessment carries unearned authority into decisions.

Standards, frameworks and further reading

Work that references a recognised framework is easier to defend, easier to hand over, and easier for a partner to consume:

EU Regulation on wholesale energy market integrity and transparency, governing publication of inside information and prohibition of market manipulation in energy markets.
ENTSO-E and ENTSOG transparency regulations, mandating publication of generation, load, flow, outage and capacity data with defined reuse conditions.
International Energy Agency energy statistics manual conventions, governing definitions, balances, units and calorific value treatment.
Joint Organisations Data Initiative reporting definitions, standardising country submissions of oil and gas production, trade and stock data.
Market abuse regimes including the EU Market Abuse Regulation, governing use and disclosure of inside information relating to energy assets and companies.
Critical national infrastructure protection regimes such as the EU NIS2 Directive and national equivalents, restricting disclosure of vulnerability information.
International humanitarian law principles of distinction and proportionality as applied to energy infrastructure as civilian objects and objects indispensable to survival.
Extractive Industries Transparency Initiative standard, governing disclosure of payments, licences and production in participating countries.

References

Primary sources and authoritative references for this entry. Publishers revise and retire material, so treat the retrieval date as part of the citation and re-check before relying on any of it in a formal product.

ENTSO-E Transparency Platform — European Network of Transmission System Operators for Electricity. Mandatory publication of European electricity generation, load, flow and outage data
Aggregated Gas Storage Inventory — Gas Infrastructure Europe. Daily European gas storage inventory and flow data by facility
Energy statistics and analysis — US Energy Information Administration. Comprehensive energy production, refining, stocks, trade and price data
Joint Organisations Data Initiative databases — JODI. Country-reported oil and gas production, demand and stock statistics
Global Energy Monitor trackers — Global Energy Monitor. Open asset-level trackers of energy infrastructure with status and location
Power Reactor Information System — International Atomic Energy Agency. Official database of nuclear reactors, status and operational history
Energy statistics and market reports — International Energy Agency. Authoritative balances, definitions and energy security analysis
Fire Information for Resource Management System — NASA. Thermal anomaly detections used to observe flaring and facility incidents
Copernicus Data Space Ecosystem — European Space Agency and European Commission. Open satellite imagery used to verify storage, construction and damage
UN Comtrade Database — United Nations Statistics Division. Bilateral trade statistics used for mirror analysis of energy flows

Link integrity: every reference above was verified with a live request when this page was generated. Where a publisher had moved or withdrawn a document, the link was repointed at a preserved copy in the Internet Archive and marked as archived. Anything with no reachable copy anywhere had its link removed rather than left to rot — the source is still credited, it simply cannot be linked.

Put it into practice

The Quantus Intel threat intelligence platform operationalises this entry: asset baselines, operator transparency feeds and imagery-verified operational status in one picture. Explore the platform, or browse the rest of the library by following any tag above.