Current research projects

Test method for high - temperature heat pump - oils

Verification of storage suitability of cryo tubes

Reducing the filling quantity

Thermostatic Expansion Valves

Brine (water)-water heat pump

Certification of efficient air conditioning and ventilation systems through the new "indoor air quality seal" for non-residential buildings

Characterisation of Superconductors in Hydrogen Atmosphere

Innovative cryogenic cooling system for the recondensation / liquefaction of technical gases up to 77 K

Production of novel barrier layers on polymer materials to reduce hydrogen permeation

Investigation according to DIN EN ISO 14903

Software for technical building equipment

3D - Air flow sensor

Development of test methods and test rigs for stationary integrated refrigeration units

Cool Up

Lifetime prediction of hermetic compressor systems

Industry 4.0 membrane heat and mass exchanger (i-MWÜ4.0)

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Optimizing HVAC operation with machine learning

BMWi Euronorm Innokom

01/2019–05/2021

Dr.-Ing. Thomas Oppelt

+49-351-4081-5321

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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