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Research Article

Building electric energy prediction modeling for BEMS using easily obtainable weather factors with Kriging model and data mining

Kibeom Ku, Shinkyu Jeong( )
Department of Mechanical Engineering, KyungHee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, R.O. Korea
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Abstract

In this article, a building electric energy prediction model using a Kriging method was developed for an efficient building energy management system (BEMS). In the prediction model, only easily obtainable weather factors such as temperature, humidity, wind speed, etc. were used as input parameters for actual application to the BEMS. In order to identify the effects of weather factors on building energy consumption, two data mining techniques were used: Analysis Of Variance (ANOVA) and Self-Organizing Map (SOM). The accuracy of the model using only easily obtain weather factors was compared with that of the model using the weather factors selected based on the results of data mining. According to the results, the building electric energy prediction model using only easily obtainable weather factors has sufficient predictive ability for BEMS. The developed building electric energy prediction model was applied to the optimization problem of charge/ discharge scheduling for an electric energy storage system. The results showed that the building electric energy prediction model has sufficient accuracy for application to the BEMS.

Keywords

data miningbuilding energy prediction modelingKriging modelbuilding energy management system (BEMS)electric energy storage system

References

 
A Azadeh, SF Ghaderi, S Sohrabkhani (2008). Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Conversion and Management, 49: 2272–2278.
Crossref Google Scholar
 
M Brown, C Barrington-Leigh, Z Brown (2012). Kernel regression for real-time building energy analysis. Journal of Building Performance Simulation, 5: 263–276.
Crossref Google Scholar
 
LG Caldas, LK Norford (2002). A design optimization tool based on a genetic algorithm. Automation in Construction, 11: 173–184.
Crossref Google Scholar
 
LG Caldas, LK Norford (2003). Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems. Journal of Solar Energy Engineering, 125: 343–351.
Crossref Google Scholar
 
T Catalina, J Virgone, E Blanco (2008). Development and validation of regression models to predict monthly heating demand for residential buildings. Energy and Buildings, 40: 1825–1832.
Crossref Google Scholar
 
AJ Conejo, JM Morales, L Baringo (2010). Real-time demand response model. IEEE Transactions on Smart Grid, 1: 236–242.
Crossref Google Scholar
 
DOE (2010). Commercial Reference Buildings. Available at http:// energy.gov/eere/buildings/commercial-reference-buildings. Accessed 16 Dec 2015.
 
B Dong, C Cao, SE Lee (2005). Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings, 37: 545–553.
Crossref Google Scholar
 
EIA (2016). International Energy Outlook 2016. Washington DC: Energy Information Administration.
 
B Eisenhower, Z O’Neill, S Narayanan, VA Fonoberov, I Mezić (2012). A methodology for meta-model based optimization in building energy models. Energy and Buildings, 47: 292–301.
Crossref Google Scholar
 
BB Ekici, UT Aksoy (2009). Prediction of building energy consumption by using artificial neural networks. Advances in Engineering Software, 40: 356–362.
Google Scholar
 
RZ Freire, GHC Oliveira, N Mendes (2008). Predictive controllers for thermal comfort optimization. Energy and Buildings, 40: 1353–1365.
Google Scholar
 
C Georgescu, A Afshari, G Bornard (1994). Optimal adaptive predictive control and fault detection of residential building heating systems. In: Proceedings of the 3rd IEEE Conference on Control Applications, Glasgow, UK.
 
C Ghiaus (2006). Experimental estimation of building energy performance by robust regression. Energy and Buildings, 38: 582–587.
Google Scholar
 
PA González, JM Zamarreño (2005). Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy and Buildings, 37: 595–601.
Google Scholar
 
I Jaffal, C Inard, C Ghiaus (2009). Fast method to predict building heating demand based on the design of experiments. Energy and Buildings, 41: 669–677.
Google Scholar
 
S-K Jeong, S Obayashi (2006). Multi-objective optimization using Kriging model and data mining. International Journal of Aeronautical and Space Sciences, 7: 1–12.
Google Scholar
 
DR Jones, M Schonlau, WJ Welch (1998). Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13: 455–492.
Google Scholar
 
SJ Kang, J Park, K-Y Oh, JG Noh, H Park (2014). Scheduling-based real time energy flow control strategy for building energy management system. Energy and Buildings, 75: 239–248.
Google Scholar
 
S Karatasou, M Santamouris, V Geros (2006). Modeling and predicting building’s energy use with artificial neural networks: Methods and results. Energy and Buildings, 38: 949–958.
Google Scholar
 
T Kohonen (1998). The self-organizing map. Neurocomputing, 21: 1–6.
Google Scholar
 
J Leadbetter, L Swan (2012). Battery storage system for residential electricity peak demand shaving. Energy and Buildings, 55: 685–692.
Google Scholar
 
Q Li, Q Meng, J Cai, H Yoshino, A Mochida (2009). Applying support vector machine to predict hourly cooling load in the building. Applied Energy, 86: 2249–2256.
Google Scholar
 
Y Ma, F Borrelli, B Hencey, B Coffey, S Bengea, P Haves (2012). Model predictive control for the operation of building cooling systems. IEEE Transactions on Control Systems Technology, 20: 796–803.
Google Scholar
 
L Magnier, F Haghighat (2010). Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Building and Environment, 45: 739–746.
Google Scholar
 
A Oudalov, R Cherkaoui, A Beguin (2007). Sizing and optimal operation of battery energy storage system for peak shaving application. In: Proceedings of 2007 IEEE Lausanne Power Tech, Lausanne, Switzerland.
 
L Pérez-Lombard, J Ortiz, C Pout (2008). A review on buildings energy consumption information. Energy and Buildings, 40: 394–398.
Google Scholar
 
J Široký, F Oldewurtel, J Cigler, S Prívara (2011). Experimental analysis of model predictive control for an energy efficient building heating system. Applied Energy, 88: 3079–3087.
Google Scholar
 
J Virote, R Neves-Silva (2012). Stochastic models for building energy prediction based on occupant behavior assessment. Energy and Buildings, 53: 183–193.
Google Scholar
 
JA Wright, HA Loosemore, R Farmani (2002). Optimization of building thermal design and control by multi-criterion genetic algorithm. Energy and Buildings, 34: 959–972.
Google Scholar
 
J Xu, J-H Kim, H Hong, J Koo (2015). A systematic approach for energy efficient building design factors optimization. Energy and Buildings, 89: 87–96.
Google Scholar
 
C-W Yan, J Yao (2010). Application of ANN for the prediction of building energy consumption at different climate zones with HDD and CDD. In: Proceedings of the 2nd International Conference on Future Computer and Communication, Wuhan, China.
 
Z Yu, F Haghighat, BCM Fung, H Yoshino (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42: 1637–1646.
Google Scholar
Building Simulation
Volume 11 Issue 4,
August 2018
Pages 739-751
DOI: 10.1007/s12273-018-0440-1
Cite this article:
Ku K, Jeong S. Building electric energy prediction modeling for BEMS using easily obtainable weather factors with Kriging model and data mining. Building Simulation, 2018, 11(4): 739-751. https://doi.org/10.1007/s12273-018-0440-1

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Received: 06 December 2017
Revised: 19 February 2018
Accepted: 23 February 2018
Published: 17 March 2018
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018
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