Listing translated from German by Talent Club.
Job description
Internship / Thesis: AI Application for Energy Management & Cloud-Edge Control
Area: 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 final thesis, you will develop an AI application for real energy data.
The local control system, for example PLC, Loxone, industrial controller or edge gateway, is located at the customer's site. From there, operational data is transmitted to a cloud portal, stored in a database and then evaluated by AI models.
Based on this evaluation, intelligent recommendations and control decisions are to be generated and sent back to the local control devices.
Project goal
Building a functional prototype for an AI-supported energy management platform with the following functions:
- Collection of energy data from local controllers
- Transmission of data to a cloud portal
- Storage and structuring in a database
- AI-based analysis of consumption, generation, storage, heat pump, wallbox and electricity prices
- Derivation of optimization decisions
- Transmission of setpoints or recommendations back to local control devices
- Implementation of decision logic 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 price
- Dynamic control of heat pumps and heating elements
- Use 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 community, direct marketing or spot market
- AI-based recommendations for operators and installation partners
- Automatic parameterization of local energy management systems
Technical tasks
- Development of a data pipeline from the local controller 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 supported decision logic
- Integration of weather, price and forecast data
- Development of an interface for transmitting control commands back
- Assessment of security, resilience 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 knowledge of programming
- 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
- First 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 on real energy data and real customer systems
- Direct collaboration with management
- Plenty of technical scope for shaping the work
- Flexible working hours
- Hybrid work possible
- Opportunity for subsequent employment
- A future-oriented topic at the interface 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 resilience 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.