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Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability

Author

Listed:
  • V. S. K. V. Harish

    (Department of Electrical Engineering, Netaji Subhas University of Technology (NSUT), Sector-3, Dwarka, Delhi 110078, India)

  • Arun Kumar

    (Department of Hydro and Renewable Energy, Indian Institute of Technology Roorkee (IITR), Haridwar, Roorkee 247667, India)

  • Tabish Alam

    (CSIR-Central Building Research Institute, Roorkee 247667, India)

  • Paolo Blecich

    (Department of Thermodynamics and Energy Engineering, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia)

Abstract

Building energy management system involves the development of control strategies for the heating, ventilation, and air-conditioning (HVAC), as well as lighting, systems. Building energy modeling is a significant part of designing such strategies. In order to analyze the feasibility of a building energy system model for any desired control strategy, a mathematical assessment tool is developed in this paper. A multi-input multi-output (MIMO) building energy system model, consisting of an outdoor wall, an external wall, two partition walls, one roof, and a ceiling, has been considered as the virtual test setup. A methodology for conducting stability and controllability assessment tests on the building energy model is proposed using inverse dynamics input theory (IDIT). IDIT enables the decoupling of control variables so as to enable the conversion of an MIMO system to a number of independent single-input single-output systems. The controllability is assessed based on the design properties for continuous systems: asymptotes and transmission zeros. The results show that the relative humidity and air temperature of the building space were controllable for all operating points; however, in unconditioned situations, where the humidity levels of the building space were greater than that of the outdoor levels, the models were unstable.

Suggested Citation

  • V. S. K. V. Harish & Arun Kumar & Tabish Alam & Paolo Blecich, 2021. "Assessment of State-Space Building Energy System Models in Terms of Stability and Controllability," Sustainability, MDPI, vol. 13(21), pages 1-26, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11938-:d:667092
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    as
    1. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    2. Daniel Plörer & Sascha Hammes & Martin Hauer & Vincent van Karsbergen & Rainer Pfluger, 2021. "Control Strategies for Daylight and Artificial Lighting in Office Buildings—A Bibliometrically Assisted Review," Energies, MDPI, vol. 14(13), pages 1-18, June.
    3. Gnana Swathika Odiyur Vathanam & Karthikeyan Kalyanasundaram & Rajvikram Madurai Elavarasan & Shabir Hussain Khahro & Umashankar Subramaniam & Rishi Pugazhendhi & Mehana Ramesh & Rishi Murugesan Gopal, 2021. "A Review on Effective Use of Daylight Harvesting Using Intelligent Lighting Control Systems for Sustainable Office Buildings in India," Sustainability, MDPI, vol. 13(9), pages 1-32, April.
    4. Alessandro Franco & Lorenzo Miserocchi & Daniele Testi, 2021. "Energy Intensity Reduction in Large-Scale Non-Residential Buildings by Dynamic Control of HVAC with Heat Pumps," Energies, MDPI, vol. 14(13), pages 1-17, June.
    5. Suman Paneru & Forough Foroutan Jahromi & Mohsen Hatami & Wilfred Roudebush & Idris Jeelani, 2021. "Integration of Emergy Analysis with Building Information Modeling," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    6. Kittisak Lohwanitchai & Daranee Jareemit, 2021. "Modeling Energy Efficiency Performance and Cost-Benefit Analysis Achieving Net-Zero Energy Building Design: Case Studies of Three Representative Offices in Thailand," Sustainability, MDPI, vol. 13(9), pages 1-24, May.
    7. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    8. Wang, Dengjia & Hu, Liang & Du, Hu & Liu, Yanfeng & Huang, Jianxiang & Xu, Yanchao & Liu, Jiaping, 2020. "Classification, experimental assessment, modeling methods and evaluation metrics of Trombe walls," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    9. Homod, Raad Z., 2018. "Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings," Renewable Energy, Elsevier, vol. 126(C), pages 49-64.
    10. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    11. Harish, V.S.K.V. & Kumar, Arun, 2016. "Reduced order modeling and parameter identification of a building energy system model through an optimization routine," Applied Energy, Elsevier, vol. 162(C), pages 1010-1023.
    12. Homod, Raad Z. & Gaeid, Khalaf S. & Dawood, Suroor M. & Hatami, Alireza & Sahari, Khairul S., 2020. "Evaluation of energy-saving potential for optimal time response of HVAC control system in smart buildings," Applied Energy, Elsevier, vol. 271(C).
    13. Panagiotis Korkidis & Anastasios Dounis & Panagiotis Kofinas, 2021. "Computational Intelligence Technologies for Occupancy Estimation and Comfort Control in Buildings," Energies, MDPI, vol. 14(16), pages 1-33, August.
    14. Giulia Lamberti & Giacomo Salvadori & Francesco Leccese & Fabio Fantozzi & Philomena M. Bluyssen, 2021. "Advancement on Thermal Comfort in Educational Buildings: Current Issues and Way Forward," Sustainability, MDPI, vol. 13(18), pages 1-29, September.
    15. Pasichnyi, Oleksii & Wallin, Jörgen & Kordas, Olga, 2019. "Data-driven building archetypes for urban building energy modelling," Energy, Elsevier, vol. 181(C), pages 360-377.
    16. Minjeong Sim & Dongjun Suh & Marc-Oliver Otto, 2021. "Multi-Objective Particle Swarm Optimization-Based Decision Support Model for Integrating Renewable Energy Systems in a Korean Campus Building," Sustainability, MDPI, vol. 13(15), pages 1-18, August.
    17. Shamsi, Mohammad Haris & Ali, Usman & Mangina, Eleni & O’Donnell, James, 2021. "Feature assessment frameworks to evaluate reduced-order grey-box building energy models," Applied Energy, Elsevier, vol. 298(C).
    18. Robert Lou & Kevin P. Hallinan & Kefan Huang & Timothy Reissman, 2020. "Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences," Sustainability, MDPI, vol. 12(5), pages 1-15, March.
    19. Yafei Zhao & Paolo Vincenzo Genovese & Zhixing Li, 2020. "Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI," Future Internet, MDPI, vol. 12(2), pages 1-18, February.
    20. Alessandro Franco & Carlo Bartoli & Paolo Conti & Lorenzo Miserocchi & Daniele Testi, 2021. "Multi-Objective Optimization of HVAC Operation for Balancing Energy Use and Occupant Comfort in Educational Buildings," Energies, MDPI, vol. 14(10), pages 1-19, May.
    21. Binju P Raj & Chandan Swaroop Meena & Nehul Agarwal & Lohit Saini & Shabir Hussain Khahro & Umashankar Subramaniam & Aritra Ghosh, 2021. "A Review on Numerical Approach to Achieve Building Energy Efficiency for Energy, Economy and Environment (3E) Benefit," Energies, MDPI, vol. 14(15), pages 1-26, July.
    22. Lee-Yong Sung & Jonghoon Ahn, 2020. "Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment," Energies, MDPI, vol. 13(5), pages 1-15, March.
    23. Domenico Campisi & Simone Gitto & Donato Morea, 2018. "An Evaluation of Energy and Economic Efficiency in Residential Buildings Sector: A Multi-criteria Analisys on an Italian Case Study," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 185-196.
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