Building Simulation

, Volume 11, Issue 4, pp 739–751 | Cite as

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

Research Article Building Thermal, Lighting, and Acoustics Modeling
  • 19 Downloads

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

building energy prediction modeling Kriging model data mining building energy management system (BEMS) electric energy storage system 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

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

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringKyungHee UniversityGyeonggi-doR.O. Korea

Personalised recommendations