Training course · ChatGPT · Energy & Utilities

ChatGPT for the Energy Sector

Use ChatGPT safely and productively at work: projects, custom GPTs, data analysis, Canvas and privacy settings in team workspaces.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“ChatGPT for the Energy Sector” is a hands-on course for roles such as Staff at municipal utilities and energy suppliers, Grid operators in planning, connections and operations and Customer service and billing teams. Rather than learning ChatGPT in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Energy & Utilities.

For many business teams, ChatGPT is the first AI tool they use every day – yet there is a methodical gap between asking the odd question and achieving real productivity gains. In this hands-on day you learn to apply ChatGPT deliberately to recurring work: drafting and editing text, summarising documents, analysing spreadsheets and structuring decision papers. You work with Projects that keep files and instructions together, with Canvas for reworking longer documents, and with data analysis for Excel and CSV files. Every exercise is based on genuine tasks from your own function.

The second focus is scale and responsibility. You build a custom GPT that knows your templates, tone of voice and review rules, and that colleagues across the workspace can use. We also clarify which data belongs in ChatGPT and which does not, how Team and Enterprise workspaces differ from personal accounts, and which settings matter for model training, memory and sharing. You leave with a personal prompt library, a working GPT and clear ground rules for using ChatGPT responsibly across your organisation.

The energy sector is undergoing a deep transformation: distributed generation, grid expansion, sector coupling, new market roles and dense regulation are reshaping municipal utilities, grid operators, suppliers and energy service providers alike. Generative AI helps above all in managing complexity. It summarises regulatory decisions and legislative changes, answers customer enquiries about billing, tariffs or grid connections efficiently, unlocks technical documentation and prepares project documents for permits. AI agents can support market communication processes, pre-sort fault reports or automate reporting. Because critical infrastructure is involved, requirements for IT security, data quality and traceability are especially high.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Staff at municipal utilities and energy suppliers
  • Grid operators in planning, connections and operations
  • Customer service and billing teams
  • Regulatory affairs and energy trading
  • Renewable energy project developers

Typical challenges we address

  • Dense regulation that changes frequently
  • Growing numbers of customer enquiries on tariffs, bills and grid connections
  • Extensive technical and permitting documentation
  • Skills shortages alongside rising investment volumes

What you will be able to do

  • You write structured prompts that reliably produce usable results for your day-to-day tasks.
  • You organise your work in Projects and revise longer documents efficiently with Canvas.
  • You analyse spreadsheets and files with data analysis and critically verify the results.
  • You build a custom GPT for a recurring task and share it with your team.
  • Navigate regulatory requirements and technical documents faster and more reliably with AI.
  • Relieve customer service and grid connection processes through AI-supported pre-checks and draft replies.
  • Assess AI use cases with due regard to IT security and critical infrastructure requirements.

Course outline

Module 1 Foundations and effective prompting
  • How language models work: strengths, limitations and hallucinations
  • Building effective prompts with role, context, task and output format
  • Refining iteratively instead of starting over: follow-ups and correction loops
  • Choosing models and using web search and deep research appropriately
Module 2 Projects, Canvas and document work
  • Setting up Projects to group files, instructions and chats by topic
  • Summarising, comparing and interrogating long documents
  • Using Canvas to revise text and drafts section by section
  • Templates for emails, reports, minutes and presentation outlines
Module 3 Data analysis with spreadsheets and files
  • Uploading, cleaning and analysing Excel and CSV files
  • Calculating KPIs, replicating pivot logic and explaining variances
  • Generating charts and preparing them for presentations
  • Verifying results: checking calculation steps and sanity checks
Module 4 Custom GPTs for recurring tasks
  • Identifying and scoping good use cases for GPTs
  • Configuring instructions, knowledge files and conversation starters
  • Testing, improving and sharing a GPT within the workspace
  • Maintenance, change tracking for instructions and ownership in the team
Module 5 Privacy, workspaces and governance
  • Personal accounts versus Team and Enterprise workspaces
  • Settings for model training, memory, temporary chats and sharing
  • Handling personal and confidential data in line with GDPR
  • Internal usage policy and quality assurance for AI output
Module 6 Practice lab: Energy & Utilities
  • Making regulation understandable
  • Relieving customer service
  • Pre-checking connection requests
  • Unlocking technical documentation
  • Permitting and project documents
  • Reporting and market analysis
Module 7 Data protection, quality & transfer to daily work
  • As operators of critical infrastructure, utilities and grid operators face heightened IT security requirements, for example under NIS2 and sector-specific security catalogues; grid and asset data must not flow uncontrolled into external AI services. Consumption and metering data is personal data under the GDPR and metering regulations, and unbundling rules must also be observed.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Making regulation understandable

Regulatory decisions, legislative changes and industry guidelines are summarised, their impact on internal processes derived and prepared as task lists for departments – reviewed by regulatory affairs.

Relieving customer service

Enquiries about instalments, annual bills, tariff changes or registering wallboxes and PV systems are classified, draft replies created and missing documents requested specifically. Recurring issues feed into FAQs and self-service content.

Pre-checking connection requests

Incoming applications are checked for completeness, technical details extracted and passed to grid planning in a structured format, with follow-up questions to installers drafted. Processing times shrink noticeably.

Unlocking technical documentation

Operating manuals, maintenance logs and excerpts from standards are made searchable so teams quickly get answers to technical questions, including a reference to the source.

Permitting and project documents

For wind, solar or grid projects, application documents are structured, comments from authorities and the public analysed and project reports prepared for boards. Deadlines and conditions are tracked clearly.

Reporting and market analysis

AI helps write commentary on sales, generation and grid KPIs, summarises market reports and drafts mandatory disclosures and internal reports. Figures always come from verified source systems, never from the AI.

Prerequisites

Confident use of office applications and a web browser; no prior AI knowledge is required. Feel free to bring your own non-confidential sample tasks from your daily work. Professional experience in an energy sector company; no programming skills required.

Technical requirements

A laptop with an up-to-date browser and a ChatGPT account; a paid plan or access to your organisation's Team or Enterprise workspace is recommended for custom GPTs, Projects and data analysis.

Data protection & compliance

As operators of critical infrastructure, utilities and grid operators face heightened IT security requirements, for example under NIS2 and sector-specific security catalogues; grid and asset data must not flow uncontrolled into external AI services. Consumption and metering data is personal data under the GDPR and metering regulations, and unbundling rules must also be observed.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Wed, 25 November 2026 Online Request
  • Thu, 3 December 2026 Munich Request
  • Tue, 8 December 2026 Online Request
  • Wed, 16 December 2026 Stuttgart Request
  • Tue, 12 January 2027 Online Request
  • Wed, 27 January 2027 Berlin Request

Frequently asked questions

Do I need a paid ChatGPT licence for the course?

You can follow the fundamentals with a free account. However, features such as custom GPTs, Projects with larger files and advanced data analysis are tied to paid plans or come with far more generous limits there. Ideally you join with access to your organisation's Team or Enterprise workspace, so you practise under real conditions.

May I enter confidential company data into ChatGPT?

That depends on your plan, your contractual terms and your internal policy. In Team and Enterprise workspaces, content is not used for training by default, but GDPR and confidentiality obligations still apply. In the course you learn how to classify and anonymise data and how to choose the right settings.

How is a custom GPT different from a saved prompt?

A GPT bundles persistent instructions, knowledge files and optional tools such as web search or data analysis into a dedicated assistant. Colleagues can use it without any prompting skills and get consistent results. A saved prompt, by contrast, has to be copied and supplemented with context every time, which is far more error-prone.

May a grid operator use cloud AI services?

In principle yes, but only for suitable data and after approval by IT security and data protection. Operational and grid data from critical assets does not belong in external services. In the course we carefully distinguish non-critical use cases such as text work from sensitive areas that require special safeguards.

Can AI reliably interpret regulatory changes?

AI summarises regulatory decisions and legislation well and helps identify affected processes. Binding interpretation, however, remains the job of regulatory affairs or the legal department. The course shows how to back up AI summaries with source references and avoid misinterpretation.

How much does “ChatGPT for the Energy Sector” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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