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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 31108

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Simplified 3R2C Building Thermal Network Model: A Case Study
Whole building energy simulation models are widely used for predicting future energy consumption, performance diagnosis and optimum control.  Black box building energy modeling approach has been heavily studied in the past decade. The thermal response of a building can also be modeled using a network of interconnected resistors (R) and capacitors (C) at each node called R-C network. In this study, a model building, Case 600, as described in the “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Program”, ASHRAE standard 140, is studied along with a 3R2C thermal network model and the ASHRAE clear sky solar radiation model. Although building an energy model involves two important parts of building component i.e., the envelope and internal mass, the effect of building internal mass is not considered in this study. All the characteristic parameters of the building envelope are evaluated as on Case 600. Finally, monthly building energy consumption from the thermal network model is compared with a simple-box energy model within reasonable accuracy. From the results, 0.6-9.4% variation of monthly energy consumption is observed because of the south-facing windows.
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[1] Ogunsola, Oluwaseyi T., and Li Song. "Review and evaluation of using RC thermal modeling of cooling load prediction for HVAC system control purpose." In ASME 2012 International Mechanical Engineering Congress and Exposition, pp. 735-743. American Society of Mechanical Engineers, 2012.
[2] Harish, V. S. K. V., and Arun Kumar. "A review on modeling and simulation of building energy systems." Renewable and Sustainable Energy Reviews 56 (2016): 1272-1292.
[3] Coakley, Daniel, Paul Raftery, and Marcus Keane. "A review of methods to match building energy simulation models to measured data." Renewable and sustainable energy reviews 37 (2014): 123-141.
[4] Li, Xiwang, and Jin Wen. "Review of building energy modeling for control and operation." Renewable and Sustainable Energy Reviews 37 (2014): 517-537.
[5] Amasyali, Kadir, and Nora M. El-Gohary. "A review of data-driven building energy consumption prediction studies." Renewable and Sustainable Energy Reviews 81 (2018): 1192-1205.
[6] Luo, Qi, and Kartik B. Ariyur. "Building thermal network model and application to temperature regulation." In Control Applications (CCA), 2010 IEEE International Conference on, pp. 2190-2195. IEEE, 2010.
[7] Xu, Xinhua, and Shengwei Wang. "Optimal simplified thermal models of building envelope based on frequency domain regression using genetic algorithm." Energy and Buildings 39, no. 5 (2007): 525-536.
[8] O'Neill, Zheng, Satish Narayanan, and Rohini Brahme. "Model-based thermal load estimation in buildings." Proceedings of SimBuild 4, no. 1 (2010): 474-481.
[9] Rahman, SM Mahbobur. Data driven models applied in building load forecasting for residential and commercial buildings. The University of Texas at San Antonio, 2015.
[10] Handbook, A. S. H. R. A. E. "Fundamentals." American Society of Heating, Refrigerating and Air Conditioning Engineers, Atlanta 111 (2001).
[11] Ji, Ying, Peng Xu, Pengfei Duan, and Xing Lu. "Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data." Applied Energy 169 (2016): 309-323.
[12] Ruya, Elvin, and Godfried Augenbroe. "The Impacts of HVAC Downsizing on Thermal Comfort Hours and Energy Consumption." Proceedings of SimBuild 6, no. 1 (2016).
[13] Harb, Hassan, Neven Boyanov, Luis Hernandez, Rita Streblow, and Dirk Müller. "Development and validation of grey-box models for forecasting the thermal response of occupied buildings." Energy and Buildings 117 (2016): 199-207.
[14] Rahman, SM Mahbobur, and P. E. Rolando Vega PhD. "Machine Learning Approach Applied in Electricity Load Forecasting: Within Residential Houses Context." ASHRAE Transactions 121 (2015): 1V.
[15] Doddi, Harish, Saurav Talukdar, Deepjyoti Deka, and Murti Salapaka. "Data-driven identification of a thermal network in multi-zone building." In 2018 IEEE Conference on Decision and Control (CDC), pp. 7302-7307. IEEE, 2018.
[16] Crawley, Drury B., Jon W. Hand, Michaël Kummert, and Brent T. Griffith. "Contrasting the capabilities of building energy performance simulation programs." Building and environment 43, no. 4 (2008): 661-673.
[17] Li, Zhengwei, and Gongsheng Huang. "Re-evaluation of building cooling load prediction models for use in humid subtropical area." Energy and Buildings 62 (2013): 442-449.
[18] Dong, Bing, Zhaoxuan Li, SM Mahbobur Rahman, and Rolando Vega. "A hybrid model approach for forecasting future residential electricity consumption." Energy and Buildings 117 (2016): 341-351.
[19] Li, Zhaoxuan, S. M. Rahman, Rolando Vega, and Bing Dong. "A hierarchical approach using machine learning methods in solar photovoltaic energy production forecasting." Energies 9, no. 1 (2016): 55.
[20] Rahman, SM Mahbobur. Data driven models applied in building load forecasting for residential and commercial buildings. The University of Texas at San Antonio, 2015.
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