A Data-driven Predictive Control Architecture for Train Thermal Energy Management
We aim to improve the energy efficiency of train climate control architectures, with a focus on a specific class of regional trains operating throughout Switzerland, especially in Zurich and Geneva. Heating, Ventilation, and Air Conditioning (HVAC) systems represent the second largest energy consumer in these trains after traction. The current architecture comprises a high-level rule-based controller and a low-level tracking controller. To improve train energy efficiency, we propose adding a middle data-driven predictive control layer aimed at minimizing HVAC energy consumption while maintaining passenger comfort. The scheme incorporates a multistep prediction model developed using real-world data collected from a limited number of train coaches. To validate the effectiveness of the proposed architecture, we conduct multiple experiments on a separate set of train coaches; our results suggest energy savings between 10% and 35% with respect to the current architecture.
Code (0)
등록된 구현이 없습니다.
Tasks
energy managementManagementMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Data-Driven Safety Preserving Control Architecture for Constrained Cyber-Physical Systems
In this paper, we propose a data-driven networked control architecture for unknown and constrained cyber-physical systems capable of detecting networked false-data-injection attacks and ensuring plant's safety. In partic…
Data-driven predictive control with estimated prediction matrices and integral action
This paper presents a data-driven approach to the design of predictive controllers. The prediction matrices utilized in standard model predictive control (MPC) algorithms are typically constructed using knowledge of a sy…
Model Predictive ControlPositionPredictionFrequency-Domain Data-Driven Predictive Control
In this paper, we propose a data-driven predictive control scheme based on measured frequency-domain data of the plant. This novel scheme complements the well-known data-driven predictive control (DeePC) approach based o…
LEMMATime SeriesData-Driven Strategies for Hierarchical Predictive Control in Unknown Environments
This article proposes a hierarchical learning architecture for safe data-driven control in unknown environments. We consider a constrained nonlinear dynamical system and assume the availability of state-input trajectorie…
Model Predictive ControlAn Uncertainty-Aware Data-Driven Predictive Controller for Hybrid Power Plants
Given the advancements in data-driven modeling for complex engineering and scientific applications, this work utilizes a data-driven predictive control method, namely subspace predictive control, to coordinate hybrid pow…