Aktuelle Forschungsprojekte

Practical training, diploma, master, bachelor

Innovative cryogenic cooling system for the recondensation / liquefaction of technical gases up to 77 K

State of system and failure analyses

Reducing the filling quantity

Verification of storage suitability of cryo tubes

Cryogenic liquid piston pumps for cold liquefied gases like LIN, LOX, LHe, LH2, LNG, LAr

Micro fluidic expansion valve

Air-flow test rig for fan characteristic measurement

Electrochemical decontamination of electrically conducting surfaces „EDeKo II“

Measurement of insulated packaging

Preformance measurements of heat exchangers

Micro heat exchangers in refrigeration

Ice Slurry Generation

Performance tests of condensing units

Humidifier System for High-Purity Gases

Development of a Cryogenic Magnetic Air Separation Unit

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

send mail

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