Grey-box model and identification procedure for domestic thermal storage vessels

被引:12
|
作者
De Ridder, Fjo [1 ,2 ]
Coomans, Mathias [1 ,2 ]
机构
[1] Flemish Inst Technol Res VITO, B-2400 Mol, Belgium
[2] EnergyVille, B-3600 Genk, Belgium
关键词
Thermal storage vessels; System identification; Modelling; Automated characterization; STRATIFICATION; PERFORMANCE; SELECTION;
D O I
10.1016/j.applthermaleng.2014.03.003
中图分类号
O414.1 [热力学];
学科分类号
摘要
This paper proposes a model and estimation algorithm, which can automatically characterize a broad range of domestic hot water cylinders and hot water storage buffers. A grey-box compartmental model takes into account the heat loss, internal heat exchange, convection and mixing dynamics associated with water storage systems. Models for these systems are often used in model-predictive controllers. The estimation algorithm is able to identify, in a robust way, the model characteristics for a diversity of storage vessels. It is based on the Markov-Chain Monte-Carlo method, which makes the procedure suited for automation since local minima in the cost function can easily be circumvented. The identification procedure is tested on four different vessels in a distributed thermal storage lab-setup. Two domestic hot water cylinders and two hot water storage buffers have been monitored in a series of charge-discharge tests. It is able to adequately reconstruct the temperature variations inside the storage vessels (errors are smaller than about 5 degrees C). This algorithm is suited for predicting the state-of-charge of thermal energy storage vessels in model based control applications. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:147 / 158
页数:12
相关论文
共 50 条
  • [31] Nonlinear Grey-Box Identification with Inflow Decoupling in Gravity Sewers
    Balla, Krisztian Mark
    Kallesoe, Carsten Skovmose
    Schou, Christian
    Bendtsen, Jan Dimon
    IFAC PAPERSONLINE, 2020, 53 (02): : 1065 - 1070
  • [32] Grey-box modelling and identification of the industrial oven of a shrink tunnel
    Previtali, Davide
    Pitturelli, Leandro
    Ferramosca, Antonio
    Previdi, Fabio
    IFAC PAPERSONLINE, 2024, 58 (15): : 55 - 60
  • [33] A Grey-box Model Based on Unscented Kalman Filter to Estimate Thermal Dynamics in Buildings
    Massano, Marco
    Macii, Enrico
    Patti, Edoardo
    Acquaviva, Andrea
    Bottaccioli, Lorenzo
    2019 IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2019 IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE), 2019,
  • [34] Black-Box versus Grey-Box LPV Identification to Control a Mechanical System
    El-Dine, Christian Paraiso Salah
    Hashemi, Seyed Mahdi
    Werner, Herbert
    2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2012, : 5152 - 5157
  • [35] Grey-box Model Identification and Fault Detection of Wind Turbines Using Artificial Neural Networks
    Rahimilarki, Reihane
    Gao, Zhiwei
    2018 IEEE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2018, : 647 - 652
  • [36] Development and validation of grey-box model for district heating station
    Czemplik, Anna
    PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2008, : 3397 - 3402
  • [37] Acceleration-based active vibration control of a footbridge using grey-box model identification
    Schauer, Thomas
    Liu, Xiaohan
    Jirasek, Robert
    Bleicher, Achim
    2017 IEEE INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM), 2017, : 910 - 915
  • [38] Identification of multi-zone grey-box building models for use in model predictive control
    Arroyo, Javier
    Spiessens, Fred
    Helsen, Lieve
    JOURNAL OF BUILDING PERFORMANCE SIMULATION, 2020, 13 (04) : 472 - 486
  • [39] Model-Based Grey-Box Fuzzing of Network Protocols
    Pan, Yan
    Lin, Wei
    Jiao, Liang
    Zhu, Yuefei
    SECURITY AND COMMUNICATION NETWORKS, 2022, 2022
  • [40] Grey-box state-space identification of nonlinear mechanical vibrations
    Noel, J. P.
    Schoukens, J.
    INTERNATIONAL JOURNAL OF CONTROL, 2018, 91 (05) : 1118 - 1139