IP4_2 Interactive Presentations
Date: Wednesday, 03 February 2021
Time: 09:00 - 09:30 CET
Virtual Conference Room: https://virtual21.date-conference.com/meetings/virtual/YyAdz2Y4a4M3EQPKY
Interactive Presentations run simultaneously during a 30-minute slot. Additionally, each IP paper is briefly introduced in a one-minute presentation in a corresponding regular session
|IP4_2.1||THERMAL COMFORT AWARE ONLINE ENERGY MANAGEMENT FRAMEWORK FOR A SMART RESIDENTIAL BUILDING
Daichi Watari, Osaka University, JP
Daichi Watari1, Ittetsu Taniguchi1, Francky Catthoor2, Charalampos Marantos3, Kostas Siozios4, Elham Shirazi5, Dimitrios Soudris3 and Takao Onoye1
1Osaka University, JP; 2imec, KU Leuven, BE; 3National TU Athens, GR; 4Aristotle University of Thessaloniki, GR; 5imec, KU Leuven, EnergyVille, BE
Energy management in buildings equipped with renewable energy is vital for reducing electricity costs and maximizing occupant comfort. Despite several studies on the scheduling of appliances, a battery, and heating, ventilating, and air-conditioning (HVAC), there is a lack of a comprehensive and time-scalable approach that integrates predictive information such as renewable generation and thermal comfort. In this paper, we propose an online energy management framework to incorporate the optimal energy scheduling and prediction model of PV generation and thermal comfort by the model predictive control (MPC) approach. The energy management problem is formulated as coordinated three optimization problems covering a fast and slow time-scale. This reduces the time complexity without a significant negative impact on the global nature and quality of the result. Experimental results show that the proposed framework achieves optimal energy management that takes into account the trade-off between the electricity bill and thermal comfort.
|IP4_2.2||ONLINE LATENCY MONITORING OF TIME-SENSITIVE EVENT CHAINS IN SAFETY-CRITICAL APPLICATIONS
Jonas Peeck, TU Braunschweig, Institute of Computer and Network Engineering, DE
Jonas Peeck, Johannes Schlatow and Rolf Ernst, TU Braunschweig, DE
Highly-automated driving involves chains of perception, decision, and control functions. These functions involve data-intensive algorithms that motivate the use of a data-centric middleware and a service-oriented architecture. As an example we use the open-source project Autoware.Auto. The function chains define a safety-critical automated control task with weakly-hard real-time constraints. However, providing the required assurance by formal analysis is challenged by the complex hardware/software structure of these systems and their dynamics. We propose an approach that combines measurement, suitable distribution of deadline segments, and application-level online monitoring that serves to supervise the execution of service-oriented software systems with multiple function chains and weakly-hard real-time constraints. We use DDS as middleware and apply it to an Autoware.Auto use case.