Image Measurements on ceiling mounted cooling systems
Image Low Temperature Tribology
Image Solar-electric Cooling
Image Laughing gas as an alternativ refrigerant for R-23
Image Helium extraction from natural gas
Image Solar Cooling
Image State of system and failure analyses
Image Thermostatic Expansion Valves
Image Prüflabor Wärmepumpen at ILK Dresden
Image Optimization of pharmaceutical freezing processes
Image Hydrogen test area at ILK Dresden
Image Membrane-based Air Conditioning
Image Components for Cold Storage Systems
Image Natural gas vehicle for long distances
Image Thermal engines
Image Low noise and non metallic liquid-helium cryostat

You are here:   /  Home


Optimizing HVAC operation with machine learning

BMWi Euronorm Innokom

01/2019–05/2021

Dr.-Ing. Thomas Oppelt

+49-351-4081-684

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

Entwicklung eines schnellen Rechenverfahrens..

..für die Auslegung von Turbomaschinen basierend auf IBM

Image

PerCO

Herstellung neuartiger Sperrschichten an elastomeren Dichtungsmaterialien zur Verminderung der Permeation des Kältemittels R744 (CO2)

Image

Energy efficiency consulting - cogeneration systems

How efficient is my refrigeration system?

Image

Cold meter

The fast way to refrigerating capacity

Image

Optimizing HVAC operation with machine learning

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


Contact

Institut für Luft- und Kältetechnik - Gemeinnützige Gesellschaft mbH
Bertolt-Brecht-Allee 20, 01309 Dresden


Secretary to the Management

+49-351-4081-520

+49-351-4081-525

Bild Zuse Mitglied Bild SIG