Abstract
Cooling load prediction is indispensable to many building energy saving strategies. In this paper, we proposed a new method for predicting the cooling load of commercial buildings. The proposed approach employs a Bayesian Network model to relate the cooling load to outdoor weather conditions and internal building activities. The proposed method is computationally efficient and implementable for use in real buildings, as it does not involve sophisticated mathematical theories. In this paper, we described the proposed method and demonstrated its use via a case study. In this case study, we considered three candidate models for cooling load prediction and they are the proposed Bayesian Network model, a Support Vector Machine model, and an Artificial Neural Network model. We trained the three models with fourteen different training data datasets, each of which had varying amounts and quality of data that were sampled on-site. The prediction results for a testing week shows that the Bayesian Network model achieves similar accuracy as the Support Vector Machine model but better accuracy than the Artificial Neural Network model. Notable in this comparison is that the training process of the Bayesian Network model is fifty-eight times faster than that of the Artificial Neural Network model. The results also suggest that all three models will have much larger prediction deviations if the testing data points are not covered by the training dataset for the studied case (The maximum absolute deviation of the predictions that are not covered by the training dataset can be up to seven times larger than that of the predictions covered by the training dataset). In addition, we also found the uncertainties in the weather forecast significantly affected the accuracy of the cooling load prediction for the studied case and the Support Vector Machine model was more sensitive to those uncertainties than the other two models.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
23 November 2018
In the HTML version of the article unfortunately the copyright holder is incorrect. It should be ?? US Government (outside the USA) 2017?. It is correct in the PDF. The publisher apologizes for the mistake.
23 November 2018
In the HTML version of the article unfortunately the copyright holder is incorrect. It should be ?? US Government (outside the USA) 2017?. It is correct in the PDF. The publisher apologizes for the mistake.
23 November 2018
In the HTML version of the article unfortunately the copyright holder is incorrect. It should be ?? US Government (outside the USA) 2017?. It is correct in the PDF. The publisher apologizes for the mistake.
23 November 2018
In the HTML version of the article unfortunately the copyright holder is incorrect. It should be ?? US Government (outside the USA) 2017?. It is correct in the PDF. The publisher apologizes for the mistake.
23 November 2018
the HTML version of the article unfortunately the copyright holder is incorrect. It should be ?? US Government (outside the USA) 2017?. It is correct in the PDF. The publisher apologizes for the mistake.
References
Alajmi A (2012). Energy audit of an educational building in a hot summer climate. Energy and Buildings, 47: 122–130.
ASHRAE (2012). ASHRAE Handbook—HVAC Systems and Equipment. Atlanta, GA, USA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers.
Ben-Nakhi AE, Mahmoud MA (2004). Cooling load prediction for buildings using general regression neural networks. Energy Conversion and Management, 45: 2127–2141.
Birdsall B, Buhl WF, Ellington KL, Erdem AE, Winkelmann FC (1990). Overview of the DOE-2 Building Energy Analysis Program, Version 2. 1D. Technical Report LBL-19735-Rev.1.
Braun JE, Chaturvedia N (2002). An inverse gray-box model for transient building load prediction. HVAC&R Research, 8: 73–99.
Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002). Choosing multiple parameters for support vector machines. Machine Learning, 46: 131–159.
Chen Y, Xu P, Chu Y, Li W, Wu Y, Ni L, Bao Y, Wang K (2017). Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Applied Energy, 196: 659–670.
Corbin CD, Henze GP, May-Ostendorp P (2013). A model predictive control optimization environment for real-time commercial building application. Journal of Building Performance Simulation, 6: 159–174.
Crawley DB, Lawrie LK, Winkelmann FC, Buhl WF, Huang YJ, et al. (2001). EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.
Deb C, Eang LS, Yang J, Santamouris M (2016). Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks. Energy and Buildings, 121: 284–297.
Delage E, Lee H, Ng AY (2006). A dynamic bayesian network model for autonomous 3D reconstruction from a single indoor image. Paper presented at the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, USA.
Denoyer L, Gallinari P (2004). Bayesian network model for semistructured document classification. Information Processing and Management, 40: 807–827.
EIA (2016). The State Energy Data System. The U.S. Energy Information Administration. Availabe at http://www.eia.gov/ state/seds. Accessed 9 Mar 2016.
Eskin N, Türkmen H (2008). Analysis of annual heating and cooling energy requirements for office buildings in different climates in Turkey. Energy and Buildings, 40: 763–773.
Gneiting T, Raftery AE (2005). Weather forecasting with ensemble methods. Science, 310: 248–249.
Hao H, Corbin CD, Kalsi K, Pratt RG (2016). Transactive control of commercial buildings for demand response. IEEE Transactions on Power Systems, 32: 774–783.
Hou Z, Lian Z (2009). An application of support vector machines in cooling load prediction. Paper presented at the International Workshop on Intelligent Systems and Applications, Wuhan, China.
Hou Z, Lian Z, Yao Y, Yuan X (2006). Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique. Applied Energy, 83: 1033–1046.
Huang S, Zuo W (2014). Optimization of the Water-cooled Chiller Plant System Operation. Paper presented at the ASHRAE/IBPSAUSA Building Simulation Conference, Atlanta, GA, USA.
Huang S, Zuo W, Sohn MD (2016a). Amelioration of the cooling load based chiller sequencing control. Applied Energy, 168: 204–215.
Huang S, Zuo W, Sohn MD (2016b). A Bayesian network model for predicting the cooling load of educational facilities. Paper presented at the ASHRAE/IBPSA-USA Building Simulation Conference, Salt Lake City, UT, USA.
Huang S, Malara ACL, Zuo W, Sohn MD (2016c). A Bayesian network model for the optimization of a chiller plant’s condenser water set point. Journal of Building Performance Simulation, doi: 10.1080/19401493.2016.1269133.
Huang S, Zuo W, Sohn MD (2017). Improved cooling tower control of legacy chiller plants by optimizing the condenser water set point. Building and Environment, 111: 33–46.
Hughes JT, Domínguez-García AD, Poolla K (2015). Virtual battery models for load flexibility from commercial buildings. Paper presented at the 48th Hawaii International Conference on on System Sciences, Hawaii, USA.
Jensen, KL, Toftum J, Friis-Hansen P (2009). A Bayesian Network approach to the evaluation of building design and its consequences for employee performance and operational costs. Building and Environment, 44: 456–462.
Kashiwagi N, Tobi T (1993). Heating and cooling load prediction using a neural network system. Paper presented at the International Joint Conference on Neural Networks, Nagoya, Japan.
Kim SH (2011). Development of robust building energy demand-side control strategy under uncertainty. PhD Thesis, Georgia Institute of Technology, USA.
Kim S, Imoto S, Miyano S (2004). Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. BioSystems, 75: 57–65.
Klein SA, Duffie JA, Beckman WA (1976). TRNSYS—A transient simulation program. ASHRAE Transactions, 82: 623–633.
Krati M (2016). Energy Audit of Building Systems: An Engineering Approach, 2nd edn. New York: CRC Press.
Kwok SSK, Yuen RKK, Lee EWM (2011). An intelligent approach to assessing the effect of building occupancy on building cooling load prediction. Building and Environment, 46: 1681–1690.
Leung MC, Tse NCF, Lai LL, Chow TT (2012). The use of occupancy space electrical power demand in building cooling load prediction. Energy and Buildings, 55: 151–163.
Li Q, Meng Q, Cai J, Yoshino H, Mochida A (2009a). Applying support vector machine to predict hourly cooling load in the building. Applied Energy, 86: 2249–2256.
Li Q, Meng Q, Cai J, Yoshino H, Mochida A (2009b). Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks. Energy Conversion and Management, 50: 90–95.
Li X, Ding L, Li Y, Xu G, Li J (2010a). Hybrid genetic algorithm and support vector regression in cooling load prediction. Paper presented at the 3rd International Conference on Knowledge Discovery and Data Mining, Phuket, Thailand.
Li X, Ding L, Lv J, Xu G, Li J (2010b). A novel hybrid approach of KPCA and SVM for building cooling load prediction. Paper presented at the 3rd International Conference on Knowledge Discovery and Data Mining, Phuket, Thailand.
Li Z, Huang G (2013). Re-evaluation of building cooling load prediction models for use in humid subtropical area. Energy and Buildings, 62: 442–449.
Ma Y, Borrelli F, Hencey B, Coffey B, Bengea S, Haves P (2012). Model predictive control for the operation of building cooling systems. IEEE Transactions on Control Systems Technology, 20: 796–803.
O’Neill Z (2014). Development of a probabilistic graphical energy performance model for an office building. Paper presented at the 2014 ASHRAE Annual Meeting, Seattle, WA, USA.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 2: 2825–2830.
Reddy TA (2011). Applied Data Analysis and Modeling for Energy Engineers and Scientists. New York: Springer US.
Sakawa M, Ushiro S, Kato K, Ohtsuka K (1999). Cooling load prediction in a district heating and cooling system through simplified robust filter and multi-layered neural network. Paper presented at the IEEE International Conference on Systems, Man, and Cybernetics, Tokyo, Japan.
Schaul T, Bayer J, Sun Y, Felder M, Sehnke F, Rückstieß T, Schmidhuber J (2010). PyBrain. Journal of Machine Learning Research, 11: 743–746.
Široky J, Oldewurtel F, Cigler J, Prívara S (2011). Experimental analysis of model predictive control for an energy efficient building heating system. Applied Energy, 88: 3079–3087.
Sun Y, Wang S, Xiao F (2013a). Development and validation of a simplified online cooling load prediction strategy for a super high-rise building in Hong Kong. Energy Conversion and Management, 68: 20–27.
Sun Y, Heo Y, Tan M, Xie H, Wu CFJ, Augenbroe G (2013b). Uncertainty quantification of microclimate variables in building energy models. Journal of Building Performance Simulation, 7: 17–32.
Thevenard D, Haddad K (2006). Ground reflectivity in the context of building energy simulation. Energy and Buildings, 38: 972–980.
Toftum J, Andersen RV, Jensen KL (2009). Occupant performance and building energy consumption with different philosophies of determining acceptable thermal conditions. Building and Environment, 44: 2009–2016.
U.S. DOE (2014). Buildings Energy Data Book. U.S. Department of Energy. Available at https://catalog.data.gov/dataset/buildingsenergy-data-book. Accessed 15 May 2016.
Walter T, Price PN, Sohn MD (2016). Uncertainty estimation improves energy measurement and verification procedures. Applied Energy, 130: 230–236.
Walter T, Sohn MD (2016). A regression-based approach to estimating retrofit savings using the Building Performance Database. Applied Energy, 179: 996–1005.
Wetter M, Bonvini M, Nouidui TS (2016). Equation-based languages—A new paradigm for building energy modeling, simulation and optimization. Energy and Buildings, 117: 290–300.
Xiao F, Zhao Y, Wen J, Wang S (2016). Bayesian network based FDD strategy for variable air volume terminals. Automation in Construction, 41: 106–118.
Xue X, Wang S, Sun Y, Xiao F (2014). An interactive building power demand management strategy for facilitating smart grid optimization. Applied Energy, 116: 297–310.
Yao Y, Lian Z, Liu S, Hou Z (2004). Hourly cooling load prediction by a combined forecasting model based on Analytic Hierarchy Process. International Journal of Thermal Sciences, 43: 1107–1118.
Yu DC, Nguyen TC, Haddawy P (1999). Bayesian network model for reliability assessment of power systems. IEEE Transactions on Power Systems, 14: 426–432.
Zhang F, Deb C, Lee SE, Yang J, Shah KW (2016). Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique. Energy and Buildings, 126: 94–103.
Zhang L, Ji Q (2011). A Bayesian network model for automatic and interactive image segmentation. IEEE Transactions On Image Processing, 20: 2582–2593.
Acknowledgements
This research was supported by the U.S. National Science Foundation under award number IIS-1633338. This research was also supported by the U.S. Department of Defense under the ESTCP program. The authors thank, Ana Carolina Laurini Malara, Marco Bonvini, Michael Wetter, Mary Ann Piette, Jessica Granderson, Oren Schetrit, Rong Lily Hu and Guanjing Lin for the support provided through the research.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Huang, S., Zuo, W. & Sohn, M.D. A Bayesian Network model for predicting cooling load of commercial buildings. Build. Simul. 11, 87–101 (2018). https://doi.org/10.1007/s12273-017-0382-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12273-017-0382-z