Research on Techniques for Building Energy Model

Dimitrios-Stavros Kapetanakis, Eleni Mangina, Donal Finn

2014

Abstract

Forecasting of building thermal and cooling loads, without the use of simulation software, can be achieved using data from Building Energy Management Systems (BEMS). Experience in building modelling has shown that data analysis is a key factor in order to produce accurate results. Commercial buildings incorporate BEMS to control the Heating Ventilation and Air-Conditioning (HVAC) system and to monitor the indoor environment conditions. Measurements of temperature, humidity and energy consumption are typically stored within BEMS. These measurements include underlying information regarding buildings thermal response. This project focuses on a novel approach for cost-effective modelling of actual data from commercial buildings, with models that can be assembled rapidly and deployed easily. This approach will constitute a practical research testbed to optimise multiple objectives related to the buildings’ energy modelling research area: i) development of a novel approach for predicting thermal and cooling loads of commercial buildings; ii) highly accurate predictions in terms of thermal and cooling loads; iii) scalability of the new approach to any commercial building and iv) minimum commissioning and maintenance effort requirements.

References

  1. Aranda, A. et al., 2012. Multiple regression models to predict the annual energy consumption in the Spanish banking sector. Energy and Buildings, Volume 49, pp. 380-387.
  2. ASHRAE, 2009. Energy Estimating and Modeling Methods. In: ASHRAE Handbook-Fundamentals. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., pp. 32.1-32.33.
  3. Aydinalp-Koksal, M. & Ugursal, V. I., 2008. Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector. Applied Energy, 85(4), pp. 271-296.
  4. Bauer, M. & Scrartezzini, J., 1998. A simplified correlation method accounting for heating and cooling loads in energy-efficient buildings. Energy and Buildings, 27(2), pp. 147-154.
  5. Catalina, T., Iordache, V. & Caracaleanu, B., 2013. Multiple regression model for fast prediction of the heating energy demand. Energy and Buildings, Volume 57, pp. 302-312.
  6. Catalina, T., Virgone, J. & Blanco, E., 2008. Development and validation of regression models to predict monthly heating demand for residential buildings. Energy and Buildings, Volume 40, pp. 1825-1832.
  7. Dombayci, A., 2010. The prediction of heating energy consumption in a model house by using artificial neural networks in Denizli-Turkey. Advances in Engineering Software, 41(2), pp. 356-362.
  8. Dong, B., Cao, C. & Lee, S. E., 2005. Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings, 37(5), pp. 545-553.
  9. Ekici, B. B. & Aksoy, U. T., 2009. Prediction of building energy consumption by using artificial neural networks. Advances in Engineering Software, 40(5), pp. 356-362.
  10. Fausett, L., 1994. Fundamentals of neural networks: architectures, algorithms, and applications. Upper Saddle River, NJ, USA: Prentice-Hall, Inc.
  11. Foucquier, A. et al., 2013. State of the art in building modelling and energy performances prediction: A review. Renewable and Sustainable Energy Reviews, Volume 23, pp. 272-288.
  12. González, P. A. & Zamarreño, J. M., 2005. Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy and Buildings, 37(6), pp. 595-601.
  13. Hou, Z. & Lian, Z., 2009. An Application of Support Vector Machines in Cooling Load Prediction. Wuhan, s.n., pp. 1-4.
  14. Kalogirou, S. A., Neocleous, C. C. & Schizas, C. N., 1997. Building heating load estimation using artificial neural networks. s.l., s.n., pp. 1-8.
  15. Kalogirou, S., Florides, G., Neocleous, C. & Schizas, C., 2001. Estimation of Daily Heating and Cooling Loads Using Artificial Neural Networks. Napoli, Clima 2000.
  16. Lam, J. C., Wan, K. K. W., Liu, D. & Tsang, C. L., 2010. Multiple regression models for energy use in airconditioned office buildings in different climates. Energy Conversion and Management, 51(12), pp. 2692-2697.
  17. Li, Q. et al., 2009. Applying support vector machine to predict hourly cooling load in the building. Applied Energy, 86(10), pp. 2249-2256.
  18. Li, Q. et al., 2009. Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks. Energy Conversion and Management, 50(1), pp. 90-96.
  19. Neto, A. H. & Fiorelli, F. A. S., 2008. Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy and Buildings, 40(12), pp. 2169-2176.
  20. Parti, M. & Parti, C., 1980. The total and appliancespecific conditional demand for electricity in the household sector. Bell Journal of Economics, Volume 11, pp. 309-321.
  21. Vapnik, V. & Cortes, C., 1995. Support-Vector Networks. Machine Learning, Volume 20, pp. 273-297.
  22. Westergren, K.-E., Högberg, H. & Norlén, U., 1999. Monitoring energy consumption in single-family houses. Energy and Buildings, 29(3), pp. 247-257.
  23. Yang, J., Rivard, H. & Zmeureanu, R., 2005. On-line building energy prediction using adaptive artificial neural networks. Energy and Buildings, 37(12), pp. 1250-1259.
  24. Zhao, H.-X. & Magoulès, F., 2012. A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6), pp. 3586- 3592.
Download


Paper Citation


in Harvard Style

Kapetanakis D., Mangina E. and Finn D. (2014). Research on Techniques for Building Energy Model . In Doctoral Consortium - DCAART, (ICAART 2014) ISBN Not Available, pages 22-30


in Bibtex Style

@conference{dcaart14,
author={Dimitrios-Stavros Kapetanakis and Eleni Mangina and Donal Finn},
title={Research on Techniques for Building Energy Model},
booktitle={Doctoral Consortium - DCAART, (ICAART 2014)},
year={2014},
pages={22-30},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={Not Available},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCAART, (ICAART 2014)
TI - Research on Techniques for Building Energy Model
SN - Not Available
AU - Kapetanakis D.
AU - Mangina E.
AU - Finn D.
PY - 2014
SP - 22
EP - 30
DO -