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Why Your Energy Data Is Working Against You, and What a Single Source of Truth Changes

Energy data fragmentation is not a storage problem. It is a decision-making problem. Most European energy companies already hold the time series data they need. They cannot get it into one consistent, trusted shape fast enough to act on it.uns one of the most mature battery markets in Europe. It is also the clearest warning of what maturity does to returns. UK battery storage revenue no longer comes from the service that built the market. It increasingly comes from trading.

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The problem is shape, not volume 

Ask a Head of IT or a CTO at a European utility where their time series data lives. The honest answer is usually: everywhere.

Meter readings sit in one system. SCADA telemetry sits in another. Market prices arrive from exchanges and vendors in a third format. Forecasts land in spreadsheets. Each source carries its own resolution, its own timestamp convention, and its own definition of a missing value.

Every axis is growing at once. Renewables reached 47.3% of EU electricity generation in 2025, according to Eurostat, and wind and solar produce high-frequency, weather-driven series rather than predictable baseload profiles. Around 60% of European households held an electricity smart meter at the end of 2024, and the European Commission has committed to accelerating the rollout further. The ENTSO-E Transparency Platform publishes pan-European market data at resolutions down to 15 minutes.

More sources. Higher frequency. Tighter reporting deadlines. The same integration architecture as five years ago.

The cost lands on the payroll before the infrastructure budget

Fragmentation is expensive in three ways, and only one of them appears in an IT business case.

Preparation time. Data teams spend up to 80% of their time preparing data that already exists, just not in the right shape (Volue analysis, drawn from PowerTSM deployments across more than 100 energy companies). That is capacity paid for as analysis and consumed as plumbing.

Decision divergence. When two teams answer the same question differently, decisions slow down. A trading desk and a grid planning team working from separate versions of the same consumption series will reach separate conclusions. Reconciling them takes days. Explaining the discrepancy to a regulator takes longer.

Key-person fragility. Custom pipelines depend on the individuals who built them. Every departure becomes an operational risk that nobody has priced.

Your trading team's data and your grid planning team's data live in different worlds. PowerTSM closes that gap automatically, in fractions of a second.

Stefan KomornyikVP Time Series Hub, Volue

What a central time series hub actually delivers

A time series hub is not a data lake with an energy label attached. Three capabilities separate the two.

Automatic harmonisation
Volue PowerTSM collects from every source simultaneously and resolves resolution, unit, and timestamp differences on ingestion. Downstream systems receive one consistent structure, whatever the origin.

Validation as a rule, not a script
Incoming values are checked against configurable business rules before they reach any downstream process. Anomalies, gaps, and implausible readings surface on arrival, rather than in a settlement dispute three months later.

Versioning by default
Every value change carries an author, a timestamp, and a reason, across all three time dimensions: historical, operational, and forecast. Audit preparation becomes a query instead of a project. For distribution businesses facing growing settlement and flexibility market obligations, that traceability is the difference between confidence and exposure.

Proven at energy-sector scale

Scale claims deserve production numbers, so here they are. PowerTSM processes more than 17 petabytes of time series data per month. It manages over 100 million time series in production, for more than 100 energy companies across 15 European countries. Performance stays linear as volumes grow. Some series run at second-level granularity, and queries return in fractions of a second, including during peak intraday trading.

Behind those numbers sits 35 years of energy domain expertise. PowerTSM joined Volue through the acquisition of HAKOM, a business built exclusively around energy time series. The domain assumptions are in the product, not in your integration layer.

Getting started without rebuilding anything

The most common objection is architectural, and it is fair: operations cannot pause for a migration.
They do not have to. The pattern that works across PowerTSM deployments is narrow and sequential.

  • Start with one use case that hurts. Usually price and position data for the trading desk, or settlement data for a distribution business.
  • Connect alongside, not instead of. The hub runs in parallel with existing systems while the outcome is proven on that single use case.
  • Measure the preparation time you recover. That number funds the next step.
  • Expand into the interfaces teams already use. Excel, Python, Grafana, Power BI, or the REST API. Adoption requires no change in working habits.

Each additional use case costs less than the last, because the hub already holds validated, versioned series. The economics improve as coverage grows, which is the opposite of how custom pipeline estates behave.

The IEA notes that digital technologies could avoid substantial global grid investment through to 2050 by extending asset lifetimes and integrating renewables more efficiently. That value depends on data that systems can act on without manual intervention.

Fix the foundation first

Every serious energy data initiative rests on one assumption: that the underlying time series are complete, consistent, and traceable. Forecasting rests on it. Flexibility management rests on it. AI rests on it entirely. Most organizations discover the assumption is wrong halfway through the project.

A single source of truth for time series is the cheapest work in the programme and the only work that compounds. Every use case added afterwards inherits the benefit.

Benchmark it against your own use case

The PowerTSM Proof of Value programme tests the platform against your data, your workflows, and your technical requirements, before any commercial commitment. If an internal build performs better, you will know. If PowerTSM performs better, you will know that too.

Explore Volue PowerTSM

Frequently Asked Questions

A time series hub is a central platform that collects time series data from every source in an energy business, harmonizes it on ingestion, validates it against configurable rules, and distributes one consistent version to downstream systems. Volue PowerTSM is a time series hub built specifically for the energy sector.

Fragmented data forces teams to reconcile competing versions of the same series before analysis begins. Trading, forecasting, and grid planning reach different conclusions from identical underlying measurements. Preparation consumes capacity budgeted for analysis, and audit trails become difficult to reconstruct under regulatory scrutiny.

Volue analysis across PowerTSM deployments at more than 100 energy companies indicates that data teams spend up to 80% of their time preparing data that already exists, simply not in the right shape. Centralizing harmonization and validation returns most of that capacity to analysis.

A data lake stores data in its original form and leaves interpretation to each consumer. A time series hub harmonizes resolution, units, and timestamps on ingestion, validates values against configurable rules, and versions every change. Volue PowerTSM delivers decision-ready series rather than raw files.

Yes. Volue PowerTSM deployments typically connect the hub alongside existing systems, starting with one use case such as trading price data or distribution settlement. Teams reach the data through Excel, Python, Grafana, Power BI, or the REST API, so working habits stay unchanged.

Volue PowerTSM processes more than 17 petabytes of time series data per month and manages over 100 million time series in production for more than 100 energy companies across 15 European countries. Performance stays linear as volumes grow, with query responses in fractions of a second.