Job Description

Internship / Thesis: AI Application for Energy Management & Cloud-Edge Control

Field: AI, Energy Management, IoT, Cloud, Building Automation
Location: Bludenz / Hybrid possible
Type: Internship, Bachelor's Thesis or Master's Thesis


Your Project
As part of your internship or thesis, you will develop an AI application for real energy data.
The local control system, such as PLC, Loxone, industrial controllers, or edge gateways, is located at the customer's site. From there, operational data is transmitted to a cloud portal, stored in a database, and subsequently analyzed by AI models.
Based on this analysis, intelligent recommendations and control decisions are generated and sent back to the local control devices.

Project Goal
Development of a functional prototype for an AI-powered energy management platform with the following features:
- Collection of energy data from local control systems
- Transmission of data to a cloud portal
- Storage and structuring in a database
- AI-based analysis of consumption, generation, storage, heat pumps, wallboxes, and electricity prices
- Derivation of optimization decisions
- Retransmission of setpoints or recommendations to local control devices
- Implementation of decision logics for behind-the-meter and front-of-the-meter applications

Possible Use Cases
- Optimization of battery storage based on PV generation, load profile, and electricity prices
- Dynamic control of heat pumps and heating elements
- Utilization of PV surplus for wallboxes, storage, and thermal consumers
- Forecasting of consumption, PV generation, and load peaks
- Detection of malfunctions or inefficient operating states
- Decision logic for self-consumption, feed-in, energy communities, direct marketing, or spot market
- AI-based action recommendations for operators and installation partners
- Automatic parameterization of local energy management systems

Technical Tasks
- Development of a data pipeline from local control to the cloud
- Definition of suitable data models for energy data
- Setup or expansion of a cloud database
- Development of AI/ML models or rule-based decision logics
- Integration of weather, price, and forecast data
- Development of an interface for retransmitting control commands
- Evaluation of security, fault tolerance, and local autonomy
- Documentation of the architecture and results

Possible Technologies
- Python
- MQTT
- REST API
- Modbus TCP
- OPC UA
- SQL / Time-Series Databases
- Docker
- Cloud Platforms
- Machine Learning
- Large Language Models
- LangChain / LangGraph
- OpenAI API or local AI models
- Edge Gateways
- PLC / Automation Technology
- Loxone / Building Automation

Requirements

You are studying, for example:
- Computer Science
- Software Engineering
- Data Science
- Artificial Intelligence
- Electrical Engineering
- Mechatronics
- Energy Technology
- Automation Technology
- Smart Systems

You bring:
- Interest in AI, energy, and automation
- Basic programming skills
- Understanding of data, interfaces, and technical systems
- Independent and structured way of working
- Motivation to work on a real product with practical relevance

Nice to have
- Experience with Python
- Experience with databases
- Knowledge of MQTT, REST, Modbus, or OPC UA
- Interest in PV, battery storage, heat pumps, or wallboxes
- Initial experience with machine learning or AI tools
- Understanding of cloud architectures or IoT systems

Additional Information

What we offer
- A practice-oriented thesis or internship topic with real market potential
- Work with real energy data and real customer systems
- Direct collaboration with the management
- Plenty of technical design freedom
- Flexible working hours
- Hybrid work possible
- Opportunity for later employment
- A future topic at the intersection of AI, energy, cloud, and automation


Why this topic is exciting

The future of energy management lies in the combination of local, robust control and intelligent cloud optimization.
Local control devices ensure fault tolerance and real-time capability. The cloud enables overarching analyses, forecasts, AI decisions, and optimizations beyond individual systems.
Your work can thus make a direct contribution to the next generation of intelligent energy systems.