lunduniversity.lu.se

Automatic Control

John C. Doyle ny hedersdoktor vid LTH

John C. Doyle är professor och forskare vid California Institute of Technology med fokus på dynamiska kontroll- och styrsystem. Han är författare till ledande läroböcker och hans insatser för LTH har tagit sig uttryck i ett flertal besök, seminarier och kurser i Lund. John C. Doyle har också möjliggjort ett aktivt utbyte av forskare mellan California Institute of Technology och LTH. Ett tiotal av doktoranderna från Reglerteknik vid LTH har varit på forskarutbyten hos Doyle, och nästan lika många av Doyles doktorander har gästforskat i Lund. Flera av de tidigare doktoranderna är nu professorer på olika internationella lärosäten.

Motiveringen från LTH lyder: ”John C. Doyle har en världsledande position, inte bara inom reglerteknik, utan generellt när det gäller de matematiska grundvalarna för komplexa dynamiska system inom biologi, medicin, ekologi, fysik och neurovetenskap. Han är mycket välciterad och har fått flera av de främsta utmärkelser som finns i hans forskningsfält.”

MSc. presentation by Sundström & Lindström: Physics-enhanced machine learning for energy systems

Simulation results of temperature and wall temperature and the real temperature.

Seminarium

From: 2022-05-30 16:15 to 17:15
Place: Seminar Room KC 3N27 and Zoom https://lu-se.zoom.us/j/64046568866?pwd=MVQ2VTBsWGo1TkhSREh1TjVsTzdJQT09
Contact: felix [dot] agner [at] control [dot] lth [dot] se
Save event to your calendar


Emil Sundström and Henrik Lindström are defending their Master's thesis at the Dept. of Automatic Control.

Where: Seminar room KC 3N27 and Zoom https://lu-se.zoom.us/j/64046568866?pwd=MVQ2VTBsWGo1TkhSREh1TjVsTzdJQT09
When: 30th of May, 16:15-17:15
Authors: Emil Sundström, Henrik Lindström
Title: Physics-enhanced machine learning for energy systems
Advisors: Pauline Kergus, Felix Agner, and Anders Rantzer
Examiner: Bo Bernhardsson


Abstract:
Building operations account for a huge amount of energy usage and the HVAC (Heating, Ventilation and Air Conditioning) systems are the largest consumer of energy in this sector. A way to reduce this demand is to implement more effective control algorithms and a popular way to do this is by using model based control strategies. However, this demands a precise model. Modelling heat in buildings is a difficult task since a lot of disturbances occur in the process. Computers running, lots of people in a room and an open window are all examples of disturbances that exists and have to been taken into account.

This thesis aims to use recent data-driven methods to find suitable procedures to model heat in buildings. This was done in two steps. First, a gray box model was created and its parameters fitted using different data-driven methods. Then, more complex learning-based models were tried out and added to the gray box part to catch some of the disturbances. Feed-forward neural networks, LSTM networks and box jenkins models were the methods used for this disturbance modelling part.

The results showed that a gray box model can capture most of the dynamics of the heat dynamics in the building, but that the obtained parameters and the performance depended a lot of the method used for parameter estimation. Adding a more complex disturbance part to the gray box model improved the results by far and were able to catch some of the disturbances.