Current research projects

Image Test rigs for refrigeration and heat pump technology
Image Low noise and non metallic liquid-helium cryostat
Image IO-Scan - Integral measuring optical scanning method
Image Industry 4.0 membrane heat and mass exchanger (i-MWÜ4.0)
Image Pulse-Tube-Refrigerator with sealed compressor
Image Innovative cryogenic cooling system for the recondensation / liquefaction of technical gases up to 77 K
Image Lifetime prediction of hermetic compressor systems
Image Thermostatic Expansion Valves
Image Panel with indirect evaporative cooling via membrane
Image Reduction of primary noise sources of fans
Image Optimizing HVAC operation with machine learning
Image Software for test rigs
Image Energy efficiency consulting - cogeneration systems
Image High temperature heat pump
Image Low Temperature Measuring Service
Image Cold meter

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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

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

Interested?

Please get in touch with us if you are interested in a cooperation: klima@ilkdresden.de

 


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Further Projects - Software Development

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