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Thermal transients simulations of a building by a dynamic model based on thermal-electrical analogy: Evaluation and implementation issue

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  • Capizzi, Giacomo
  • Sciuto, Grazia Lo
  • Cammarata, Giuliano
  • Cammarata, Massimiliano

Abstract

The thermal behaviour of buildings in transient conditions is established by the temperature evolution of the external environment. Determining the thermal evolution of buildings is of crucial importance for building energy design, as well as for energetic performance evaluation. Building energy performance evaluation and calculation of energy use for space heating and cooling can be carried out by several methods of various degrees of complexity and accuracy. These methods are implemented in such simulation codes as NBLSD, Il DOE-II, ENERGY PLUS and others. In order to comply with the Energy Performance of Buildings Directive (EPBD), the studies and research in this field start from the Directive 91/2002/CE and subsequently lead to the EN ISO 13790:2007. The latter proposes a thermal model for a building composed of five resistances and one capacity, called R5C1, and also offers its dynamic solution with a simple hourly computational model. The present paper suggests the solution and dynamic simulation of the R5C1 model and an evaluation of its use in building energy design. Finally, a case study regarding a typical average day in June in Catania (Sicily, Italy) is presented. The implemented model and the relative simulation results have confirmed the advantages of such a solution and have been validated for some modules of the CONPHOEBUS scrl Research Building in Catania. The proposed model has the advantage of a small number of parameters (5 thermal resistances and 1 thermal capacity) and has a simple formulation and then requires low computational resources. Furthermore, this model allows the correct estimation of the user profile with few and not sufficiently precise input data. Due to the above listed properties the proposed model was adopted by the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) for an extensive energy consumption simulation campaign involving 5000 buildings in Italy (a campaign of measurements on 20,000 buildings planned for 2017) for the calculation of statistical consumption data for public buildings.

Suggested Citation

  • Capizzi, Giacomo & Sciuto, Grazia Lo & Cammarata, Giuliano & Cammarata, Massimiliano, 2017. "Thermal transients simulations of a building by a dynamic model based on thermal-electrical analogy: Evaluation and implementation issue," Applied Energy, Elsevier, vol. 199(C), pages 323-334.
  • Handle: RePEc:eee:appene:v:199:y:2017:i:c:p:323-334
    DOI: 10.1016/j.apenergy.2017.05.052
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    1. Hong, Tianzhen & Piette, Mary Ann & Chen, Yixing & Lee, Sang Hoon & Taylor-Lange, Sarah C. & Zhang, Rongpeng & Sun, Kaiyu & Price, Phillip, 2015. "Commercial Building Energy Saver: An energy retrofit analysis toolkit," Applied Energy, Elsevier, vol. 159(C), pages 298-309.
    2. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2015. "Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, Elsevier, vol. 144(C), pages 261-275.
    3. Liang, Xin & Hong, Tianzhen & Shen, Geoffrey Qiping, 2016. "Improving the accuracy of energy baseline models for commercial buildings with occupancy data," Applied Energy, Elsevier, vol. 179(C), pages 247-260.
    4. 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.
    5. Kohler, M. & Blond, N. & Clappier, A., 2016. "A city scale degree-day method to assess building space heating energy demands in Strasbourg Eurometropolis (France)," Applied Energy, Elsevier, vol. 184(C), pages 40-54.
    6. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
    7. Yang, Tao & Pan, Yiqun & Mao, Jiachen & Wang, Yonglong & Huang, Zhizhong, 2016. "An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study," Applied Energy, Elsevier, vol. 179(C), pages 1220-1231.
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    Cited by:

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    3. Giovanni Barone & Annamaria Buonomano & Cesare Forzano & Adolfo Palombo, 2019. "Building Energy Performance Analysis: An Experimental Validation of an In-House Dynamic Simulation Tool through a Real Test Room," Energies, MDPI, vol. 12(21), pages 1-39, October.
    4. Zigui Jiang & Rongheng Lin & Fangchun Yang, 2018. "A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data," Energies, MDPI, vol. 11(9), pages 1-19, August.
    5. Yang, Jianming & Lin, Zhongqi & Wu, Huijun & Chen, Qingchun & Xu, Xinhua & Huang, Gongsheng & Fan, Liseng & Shen, Xujun & Gan, Keming, 2020. "Inverse optimization of building thermal resistance and capacitance for minimizing air conditioning loads," Renewable Energy, Elsevier, vol. 148(C), pages 975-986.
    6. Tian, Shen & Gao, Yuping & Shao, Shuangquan & Xu, Hongbo & Tian, Changqing, 2018. "Measuring the transient airflow rates of the infiltration through the doorway of the cold store by using a local air velocity linear fitting method," Applied Energy, Elsevier, vol. 227(C), pages 480-487.
    7. Yang, S. & Pilet, T.J. & Ordonez, J.C., 2018. "Volume element model for 3D dynamic building thermal modeling and simulation," Energy, Elsevier, vol. 148(C), pages 642-661.
    8. Haitao Wang & Fanghao Wu & Ning Lu & Jianfeng Zhai, 2023. "Comprehensive Research on the Near-Zero Energy Consumption of an Office Building in Hefei Based on a Photovoltaic Curtain Wall," Sustainability, MDPI, vol. 15(15), pages 1-17, July.

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