Current research projects

Image CFE-Test of Cooker Hoods
Image Certification of efficient air conditioning and ventilation systems through the new "indoor air quality seal" for non-residential buildings
Image Service offer for Leak Detection and Tightness Test
Image Humidifier System for High-Purity Gases
Image Cryogenic liquid piston pumps for cold liquefied gases like LIN, LOX, LHe, LH2, LNG, LAr
Image Reduction of primary noise sources of fans
Image Cool Up
Image Breakthrough Sensor for Adsorption Filters (BelA)
Image All-in-one device for freeze-drying and production of biomaterial
Image Investigation of coolants
Image Electrochemical decontamination of electrically conducting surfaces „EDeKo II“
Image Modular storage system for solar cooling
Image CO₂ GAS HYDRATES FOR SUSTAINABLE ENERGY AND COOLING SOLUTIONS
Image Range of services laboratory analyses
Image Solar Cooling
Image Test rigs for refrigeration and heat pump technology

You are here:  Home /  Research and Development


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 - Research and Development

Image

ZeroHeatPump

Performance management of small heat pumps without energy consumption

Image

Investigation of material-dependent parameters

Investigation of the permeation behavior