Sprecher
Beschreibung
The complexity of modern scientific facilities, particularly cleanrooms, requires precise control over environmental parameters such as temperature and humidity to ensure experimental accuracy. These facilities, often energy-intensive, face additional challenges due to climate change and the growing demand for energy efficiency. This drives the need for a simulation framework capable of dynamic regulation and sustainable building management solutions.
This work explores the potential for developing a digital twin framework aimed at improving building management across three key scenarios: initial device configuration, real-time reconfiguration, and anomaly detection. The digital twin would serve as a virtual model that collects real-time data from physical systems to predict behavior and optimize control. In alignment with the EU FlexRICAN project, the goal is to support energy flexibility, reduce consumption, and lower CO2 emissions.
To assess the viability of this framework, we will evaluate several methods for their applicability in the targeted scenarios. Mixed-Integer Linear Programming (MILP), using OMEGAlpes software, will be explored for discrete energy modeling, while greedy algorithms will be considered for real-time system adjustments. Additionally, machine learning, including reinforcement learning, will be assessed for its adaptability in dynamic environments and effectiveness in anomaly detection. This evaluation will provide insights into the strengths and limitations of each approach in improving energy efficiency and environmental management in scientific facilities.
