Building operations contribute to approximately one-third of global CO² emissions. A promising strategy to reduce these emissions by about 30% is to equip buildings with advanced control systems that optimize energy consumption in relation to renewable energy generation. However, an advanced control system requires an accurate mathematical model that captures the underlying dynamics of the building. Due to the unique characteristics of each building, a new model must be developed for every building. In my dissertation, I employ AI to leverage knowledge from existing buildings to model unknown buildings. This approach facilitates energy-efficient control across a wide range of buildings.
Fabian Raisch
Modelling and Control of Building Energy Systems using Transfer Learning
MEMBER IN THE JOINT ACADEMIC PARTNERSHIP
since
Supervisor Rosenheim Technical University of Applied Sciences:
Prof. Dr. rer. pol Benjamin Tischler
Mit der Forschungsgruppe AI4Energy treibt Benjamin Tischer die Energiewende durch datenbasierte Modellierung und KI-basierte Regelung von Energiesystemen voran. Besonderer Fokus liegt auf der Nutzung von Sensordaten zur Modellierung von Gebäude- und Quartiers-Energiesystemen. Ziel ist es den Energieverbrauch, Energiekosten und CO2-Emissionen zu senken und die Netzdienlichkeit zu verbessern.
Forschungsschwerpunkte:
- KI-basierte Modellierung und Regelung von Energiesystemen
- KI-basierte Sprachverarbeitung (Large Language Models) insb. zur Prozessenautomatisierung in Verwaltung und Entscheidungsunterstützung
- Zeitreihen- und Paneldatenstatistik
Project:
Modelling and Control of Building Energy Systems using Transfer Learning
Supervisor Technical University of Munich:
Prof. Dr. rer. pol. Christoph Goebel
Project:
Modelling and Control of Building Energy Systems using Transfer Learning
