DEEN
Thermometer

Aktuelle Forschungsprojekte

Image Calibration of Low Temperature Sensors
Image Investigation of materials
Image In-situ investigation concerning the swelling behaviour of polymer materials under elevated pressures and temperatures
Image Innovative cryogenic cooling system for the recondensation / liquefaction of technical gases up to 77 K
Image Ionocaloric cooling
Image Low noise and non metallic liquid-helium cryostat
Image Innovative Manufacturing Technologies for Cryosorption Systems
Image Cryostats, Non-Metallic and Metallic
Image Tribological investigations of oil-refrigerant-material-systems
Image Performance tests of refrigerant compressors
Image Heat2Power
Image Electrical components in refrigeration circuits
Image Innovative small helium liquefier
Image Test procedures for electrical components
Image Practical training, diploma, master, bachelor
Image Ice Slurry Generation

You are here:  Home /  Research and Development


Optimizing HVAC operation with machine learning

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

Mind map for project KT-BOT.ML
The mind map schematically shows the project approach: Linking BIM data and machine learning will lead to a tool for optimizing HVAC operation. This will be validated during the project experimentally and my means of a digital twin.

Interested?

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

 

show full article

Your Request

Further Projects - Research and Development

Image

Micro heat exchangers in refrigeration

3D-printing of micro heat exchangers

Image

Electrochemical decontamination of electrically conducting surfaces „EDeKo II“

Improvement of sanitary prevention by electrochemical decontamination

Image

In-situ investigation concerning the swelling behaviour of polymer materials under elevated pressures and temperatures

Material Science

Image

Electrical components in refrigeration circuits

High voltage tests under real conditions

Image

Influenced melting point of water by magnetic field

Controlled sub-cooling of products in freezing processes


Data Privacy Settings

Cookies are used on our website. Some of them are mandatory, while others enable us to improve your user experience on our website.

Get more info about used cookies