Current research projects

Image Influenced melting point of water by magnetic field
Image All-in-one device for freeze-drying and production of biomaterial
Image Pulse-Tube-Refrigerator with sealed compressor
Image Intelligent innovative power supply for superconducting coils
Image Multifunctional electronic modules for cryogenic applications
Image Combined building and system simulation
Image Optimizing HVAC operation with machine learning
Image Cold meter
Image Performance tests of refrigerant compressors
Image Low noise and non metallic liquid-helium cryostat
Image Hydrogen and methane testing field at the ILK
Image Cryogenic liquid piston pumps for cold liquefied gases like LIN, LOX, LHe, LH2, LNG, LAr
Image Innovative small helium liquefier
Image Helium extraction from natural gas
Image Non- invasive flow measurements
Image Measurement of insulated packaging

You are here:   /  Home


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

 


Your Request

Further Projects

Image

Low noise and non metallic liquid-helium cryostat

Low-noise Magnetic Field Cryostat for SQUID-Applications

Image

Cryostats, Non-Metallic and Metallic

position indenpendent, highest endurance, tiltable for liquid helium and liquid nitrogen

Image

Calibration of Low Temperature Sensors

According to the comparative measurement method

Image

High Capacity Pulse Tube Cooler

for Cryogenic High-Power Applications