Current research projects

Cryostats, Non-Metallic and Metallic

Micro heat exchangers in refrigeration

Energy efficiency consulting - cogeneration systems

Development of a Cryogenic Magnetic Air Separation Unit

Certifiable connection types in cryogenics

Practical training, diploma, master, bachelor

Preformance measurements of heat exchangers

Characterisation of Superconductors in Hydrogen Atmosphere

Reducing the filling quantity

Innovative Manufacturing Technologies for Cryosorption Systems

Influenced melting point of water by magnetic field

Micro fluidic expansion valve

Test rigs for refrigeration and heat pump technology

Hydrogen and methane testing field at the ILK

Overall System Optimization of Refrigeration Plant Systems for Energy Transition and Climate Protection

Multifunctional electronic modules for cryogenic applications

You are here: Home / Research and Development

Optimizing HVAC operation with machine learning

BMWi Euronorm Innokom

01/2019–05/2021

Dr.-Ing. Thomas Oppelt

+49-351-4081-5321

send mail

in progress

Intelligent control of HVAC systems – high comfort with low energy demand

Motivation

During operation, the energy efficiency of many HVAC systems remains considerably below the value predicted when planning. One reason is that especially complex systems with multiple generators, storages and consumer locations frequently are not operated optimally.

Aim of the project

Development of a tool for optimizing the operation of HVAC systems which uses machine learning (ML) methods and data from the digital building model (Building Information Model, BIM):

Optimization goal: high energy efficiency with at the same time high comfort for users
Saving operating costs, energy and carbon dioxide emissions due to increased efficiency
Continuous autonomous improvement of the ML algorithm by learning from new measured data with auto-adaptive reaction to changing conditions (building, system, use, smart meter for real time billing of energy and media, etc.)

Approach

Reproduction of the real system’s thermal-energetic behaviour in the machine learning system, training with BIM data, measured data and a digital twin of the real system

Application of ML methods for load forecasting (weather, usage patterns)
Automatic classification of utilisation scenarios, fault detection
Integration of available tools for efficient simulation of indoor air flows and for calculating energy demands
Co-Validation of optimization tool, experimental studies and digital twin