Exzellenz ab null Kelvin
DEEN
Thermometer

Current research projects

Image Breakthrough Sensor for Adsorption Filters (BelA)
Image Investigation of material-dependent parameters
Image All-in-one device for freeze-drying and production of biomaterial
Image Verification of storage suitability of cryo tubes
Image Test method for high - temperature heat pump - oils
Image Electrochemical decontamination of electrically conducting surfaces „EDeKo II“
Image High Capacity Pulse Tube Cooler
Image Reduction of primary noise sources of fans
Image Heat2Power
Image Solar Cooling
Image Testing of mobile leak detectors according to DIN EN 14624
Image Ionocaloric cooling
Image In-situ investigation concerning the swelling behaviour of polymer materials under elevated pressures and temperatures
Image Filter Tests
Image IO-Scan - Integral measuring optical scanning method
Image Practical training, diploma, master, bachelor

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

Multifunctional electronic modules for cryogenic applications

Electronic with less wiring effort - more than 100 sensors via one feedthrough

Image

Certifiable connection types in cryogenics

Detachable and permanent connections, adhesive bond / form closure / force closure

Image

Combined building and system simulation

Scientific analysis of thermodynamic processes in buildings and its systems

Image

Heat2Power

Refining of fuel cell waste heat

Image

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

How efficient is my refrigeration unit?


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