Thermische Optimierung und Steuerung in Räumen mit Offenem Plan unter Verwendung einer auf Physik-Informiertem Neuronalem Graphnetzwerk Basierenden Optimalen Steuerung

Details

Veröffentlichungs-Nr.

EP4645005

Aktenzeichen

EP25172775

Anmeldetag

28. April 2025

Veröffentlichung

5. November 2025

Rechtsraum

EP

IPC

G05B13/02F24F11/62G05B13/04

Abstract

Optimal control of multiple heating, ventilation, and air-conditioning units in an open-plan space demands fast and accurate thermodynamic modeling. Prior methods lack scalability required for effective control in large open-plan offices primarily due to air-mixing interactions. The present disclosure describes a physics-informed graph neural network (PI-GNN) to overcome these challenges. Specifically, thermodynamic interactions are modeled as edges between nodes that represent cells. Further, a modeling approach is used that allows explicit modeling of wall and window surface temperatures which are commonly ignored. The method of present disclosure utilizes PI-GNN as a state-estimator that employs a receding-horizon approach for optimal HVAC control. PI-GNNs are adapted for building HVAC control by incorporating a time-resetting strategy to handle time-dependent ambient conditions and therefore setpoints. The method of the present disclosure outperforms a regular PINN model and other baseline control strategies on thermal model accuracy, computation time, energy consumption, and user comfort.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien

Fachgebiete