Abstract
When representing building thermal characteristics for a large-scale district heating (DH) system, the traditional 1-order model is easy to solve but has a nonnegligible delay when conditions change. High-order models are widely used in building simulation, but their complexity and high time cost prevent them from being applied in DH simulation. The purpose of this paper is to find out an appropriate building thermal model for heating accident simulation. An unoccupied residential building located in northeast China was tested for 72 h heating outage and subsequent 32 h recovery. Based on the lumped parameter method and state space model, a high order was established by 4240 mass points to describe the tested building. By integrating mass points, the low-order models reduced the orders to eight, three, and one respectively. Frequency domain analysis revealed the similarities and differences among the models. The 1-order model showed a large deviation from the other three models. The subsequent time domain analysis of the 1-order model also simulated a root mean square error (RMSE) of 1.69, a delay of 6 h, and the lowest temperature drop during a heating outage. In both frequency and time domain analyses, the 8-order and 3-order models showed similar results approaching the high-order model, with an RMSE of approximately 0.50, a delay of 1 h, and small deviation. The simpler 3-order model could be used to estimate the indoor air temperature during a heating accident in large-scale DH systems. The further study on integrating the 3-order model and system identification method may overcome the shortcomings of model reduction.
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References
Agathokleous R, Barone G, Buonomano A, Forzano C, Kalogirou SA, Palombo A (2019). Building façade integrated solar thermal collectors for air heating: experimentation, modelling and applications. Applied Energy, 239: 658–679.
ASHRAE (2009). ASHRAE Handbook: Fundamentals. Atlanta, GA, USA: American Society of Heating Refrigerating and Air Conditioning Engineers.
Berger J, Gasparin S, Dutykh D, Mendes N (2020). On the comparison of three numerical methods applied to building simulation: Finite-differences, RC circuit approximation and a spectral method. Building Simulation, 13: 1–18.
Bertagnolio S, Lebrun J (2008). Simulation of a building and its HVAC system with an equation solver: Application to benchmarking. Building Simulation, 1: 234–250.
Cao S, Wang P, Wang W, Yao Y (2017). Reliability evaluation of existing district heating networks based on a building’s realistic heat gain under failure condition. Science and Technology for the Built Environment, 23: 522–531.
Clarke JA (2001). Energy Simulation in Building Design, 2nd edn. Oxford, UK: Butterworth-Heinemann.
Gao Y, Roux JJ, Teodosiu C, Zhao LH (2004). Reduced linear state model of hollow blocks walls, validation using hot box measurements. Energy and Buildings, 36: 1107–1115.
Gao Y, Fan R, Zhang Q, Roux JJ (2014). Building dynamic thermal simulation of low-order multi-dimensional heat transfer. Journal of Central South University, 21: 293–302.
Gouda MM, Danaher S, Underwood CP (2000). Low-order model for the simulation of a building and its heating system. Building Services Engineering Research and Technology, 21: 199–208.
Gouda MM, Danaher S, Underwood CP (2002). Building thermal model reduction using nonlinear constrained optimization. Building and Environment, 37: 1255–1265.
Kazanavičius E, Mikuckas A, Mikuckienė I, Čeponis J (2006). The heat balance model of residential house. Information Technology and Control, 35: 391–396.
Kimura K (1982). Theoretical Basis of Architectural Equipment. Beijing: China Architecture & Building Press. (in Chinese)
Kong Q, He X, Jiang Y (2017). Fast simulation of dynamic heat transfer through building envelope via model order reduction. Building Simulation, 10: 419–429.
Kuo BC, Golnaraghi F (1995). Automatic Control Systems. Englewood Cliffs, NJ, USA: Prentice-Hall.
MOHURD (2017). Urban Central Heating. In: China Urban-Rural Construction Statistical Yearbook. Ministry of Housing and Urban-Rural Development of China. Available at http://www.mohurd.gov.cn. Accessed 12 Feb 2019.
Ramallo-González AP, Eames ME, Coley DA (2013). Lumped parameter models for building thermal modelling: An analytic approach to simplifying complex multi-layered constructions. Energy and Buildings, 60: 174–184.
Rodríguez Jara EÁ, Sánchez de la Flor FJ, Álvarez Domínguez S, Molina Félix JL, Salmerón Lissén JM (2016). A new analytical approach for simplified thermal modelling of buildings: Self-Adjusting RC-network model. Energy and Buildings, 130: 85–97.
Rouchier S, Jiménez MJ, Castaño S (2019). Sequential Monte Carlo for on-line parameter estimation of a lumped building energy model. Energy and Buildings, 187: 86–94.
Shan X, Wang P, Lu W (2017). The reliability and availability evaluation of repairable district heating networks under changeable external conditions. Applied Energy, 203: 686–695.
Underwood CP (2014). An improved lumped parameter method for building thermal modelling. Energy and Buildings, 79: 191–201.
Wang X (2004). Study on reliability and faults condition of multi-heat-source ring-shaped heat-supply network of hot water. PhD Thesis, Harbin Institute of Technology, China. (in Chinese)
Wang Z, Lin B, Zhu Y (2015). Modeling and measurement study on an intermittent heating system of a residence in Cambridgeshire. Building and Environment, 92: 380–386.
Xie X, Jiang Y, Xia J (2008). A new approach to compute heat transfer of ground-coupled envelope in building thermal simulation software. Energy and Buildings, 40: 476–485.
Xu B, Fu L, Di H (2007). Simulation of room thermal dynamics with thermostatic temperature control. Journal of Tsinghua University (Science and Technology), 2007(06): 757–760. (in Chinese)
Yan D, Xia J, Tang W, Song F, Zhang X, Jiang Y (2008). DeST—An integrated building simulation toolkit Part I: Fundamentals. Building Simulation, 1: 95–110.
Zhou L, Bai M, Lü JZ, Cui W (2010). Theoretical solution of transient heat conduction problem in one-dimensional double-layer composite medium. Journal of Central South University of Technology, 17: 1403–1408.
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This work was supported by the National Natural Science Foundation of China (No. 51508139).
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Wang, P., Ju, Y., Liu, S. et al. Reduction analysis of building thermal models for simulation of heating accidents. Build. Simul. 13, 1249–1258 (2020). https://doi.org/10.1007/s12273-020-0654-x
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DOI: https://doi.org/10.1007/s12273-020-0654-x