Master Thesis: Model Predictive Control–Based Energy Management System (EMS) for an Office Building

With the increasing demand for residential comfort and the rising importance of energy cost optimization, it is a major challenge to balance comfort, economy, and sustainable energy usage in residential buildings. Advanced energy management systems (EMS) provide a practical solution for achieving this goal.

This thesis will develop a Model Predictive Control (MPC)–based EMS for a data-driven building model, created using neural networks in MATLAB or Python. The building model will represent a modular office building, including thermal zones, a HVAC system, a static heating system, a cold water storage tank, and an electric chiller for cooling. The MPC-based EMS will optimize pre-heating in winter and pre-cooling in summer, taking into account electricity prices, including the contribution of a photovoltaic (PV) system, and the operation of thermal storage and cooling equipment.

Tasks:

• Development of a neural network–based building model capable of predicting thermal dynamics for heating and cooling scenarios
• Design and implementation of a Model Predictive Control–based EMS for Winter and Summer operations, Considering:
  • Pre-heating and pre-cooling strategies
  • Cold water storage and electric chiller operation
  • Static heating System
  • HVAC System
  • Dynamic electricity prices and PV generation

Qualification:

• Knowledge in energy system analysis, building modeling, and control
• Experience with MATLAB/Simulink or Python (essential for neural network modeling and MPC implementation)
• Knowledge in machine learning (neural networks) and predictive control concepts
• Mechanical, Electrotechnical, or Informatics background

Apply online via this platform or by email: personal@hansa-klima.de

We offer individualized support from highly qualified and dedicated professionals within an innovative, medium-sized company.

We look forward to receiving your application.
personal@hansa-klima.de