Skip to content
PERINGER Data Solutions, back to home

Back

Enterprise

Energy and utilities

Meters, sensors, supervisory systems and market portals already produce data. The challenge is to reconcile useful sources under shared definitions, ownership and controls without confusing a data platform with a control system.

The sector’s needs

Delivered across Switzerland, remote-first, with on-site days by arrangement.

Scope boundary

The work covers agreed identities, data platforms, exchanges and office networks. OT, automation, safety and control systems require separate qualification and, where appropriate, an industrial specialist.

Examples of what gets built

  • Consumption and production forecasting

    Forecasting at the granularity and horizon that genuinely inform a decision, with factor contributions shown next to the figure.

    Predictive analysis →
  • A unified data foundation

    Meters, supervision, billing and market data brought together with one documented, defensible definition per quantity.

    The data foundation →
  • Exchanges with market partners

    Meter readings, supplier switches and billing data exchanged between your systems and the sector’s portals, with retries and a log.

    Connectors between your systems →
  • Deviation detection on measurements

    Unusual profiles reported, with the context assembled so an operator judges from one screen.

    Predictive analysis →
  • Regulatory reporting

    Datasets produced by the same pipeline as everything else, with history replayable when a rule changes retroactively.

    Automated reporting →
  • Assistant on technical documentation

    Diagrams, procedures and equipment sheets queryable from the field, with the source cited.

    Secure AI assistants →
  • Search across plant documentation

    Diagrams, test reports and operating procedures found by their content, including in scanned documents.

    Document search →
  • Chaining operational tasks

    Readings, checks and notifications triggered by the state of the system, with an operator deciding on deviations.

    Business agents →
  • Cost of the measurement platforms

    Time-series storage and forecast computation attributed to what they serve, with retention decided rather than inherited.

    Cloud cost optimisation →
  • Records from remote sites

    Jobs, readings and photographs captured on site and synced on the way back, where connectivity is a constraint rather than an assumption.

    Capture in the field →
  • Changing hypervisor in waves

    The estate carrying supervision and operational tooling moves after a pilot, with the windows, criteria, dependencies, restore and rollback defined. The possible disruption gets stated, not denied.

    Leaving proprietary virtualisation →
  • Compute and storage near the sites

    Whatever has to stay reachable when the link drops runs locally, with off-site backup and rebuilding described in code.

    Private servers and private cloud →
  • Handing over operations

    Diagram, inventory, dependencies and procedures written for an operator taking over without having followed the project, remote sites included.

    The operations file →
  • Access to operational data assigned by role

    Exchanges with the operational systems, the market portals and the partners run through inventoried technical accounts, each with an owner and a rotation date. A departure closes the access the same day.

    Identity, directory and access →
  • The data platform restored within a known time

    Measurement history and forecasts cannot be recreated from the meters. The isolated copy and the timed restore give management a known downtime before the incident, not during it.

    Backup and recovery →

Examples of solutions

Relevant experience

Assignments I delivered, described without naming anyone. No logos and no testimonials: what I did, and with what.

  • Data-pipeline architecture on Google Cloud for an international index and financial-data provider

    Airflow · dbt · BigQuery · Terraform

  • Azure data pipelines for a European solar-energy group

    Data Factory · Graph API · Business Central

  • Data-access tooling for a Swiss energy producer

    Python · R · API

Why me

  • Pipelines delivered for a European solar-energy group and for a Swiss energy producer
  • Habit of real time series, with their gaps, their duplicates and their unit changes
  • A sense of the line between information systems and operational systems, which is not crossed for a dashboard
  • Output delivered into the tool already in place rather than into one more dashboard

Test the fit: Energy and utilities

Describe the context, constraints and decision you need to make. The first conversation qualifies scope, boundaries and the next useful step.

Describe the situation