Hourly Prediction of Building Energy Consumption: An Incremental ANN Approach



Abstract Views
597

Downloads
599

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Jun 22, 2017
Published Jul 14, 2017
10.24018/ejeng.2017.2.7.397

Abstract viewed = 597 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

Rapid growth of buildings energy consumption puts the focus to improve energy efficiency by building engineers and operators. Energy management through forecasting approaches using machine-learning algorithms is an increasing research domain. Most of algorithms focus on predicting energy consumption when a considerable amount of past-observed data exist.  In this paper, we focus on the case when small amount of available data exist and the amount of data increases incrementally by time. Artificial Neural Networks used as the learning algorithm take as the training data mini batches of different sizes. Algorithm is evaluated on different batch sizes and compared to baseline learner.
Keywords: Energy Prediction Incremental Learning Neural Networks Energy Efficiency

Downloads

Download data is not yet available.

References

  1. IEA, "World energy outlook 2015. International Energy Agency; September 2015".
     Google Scholar
  2. O. Zavalani and Y. Luga, "Energy and Water Saving Possibilities in Public Facilities in Albania," in Fourth UKSim European Symposium on Computer Modeling and Simulation (EMS), Pisa, Italy, 2010.
     Google Scholar
  3. CABA, " North American Intelligent Buildings Roadmap," CABA, 2011.
     Google Scholar
  4. A. Azadeh, S. Ghaderi and S. Sohrabkhani, "Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors," Energy Conversion and Management, vol. 49, no. (8), pp. 2272 -- 2278, 2008.
     Google Scholar
  5. S. L. Wong, K. K. Wan and T. N. Lam, "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, vol. 87, no. 2, pp. 551-557, 2010.
     Google Scholar
  6. S. Kalogirou and M. Bojic, "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, vol. 25, no. (5), pp. 479 -- 491.
     Google Scholar
  7. R. Yokoyama, T. Wakui and R. Satake, "Prediction of energy demands using neural network with model identification by global," Energy Conversion and Management, vol. 50, no. 2, pp. 319-327, 2009.
     Google Scholar
  8. A. Ben-Nakhi and M. Mahmoud, "Cooling load prediction for buildings using general regression neural networks," Energy Conversion and Management, vol. 45, no. 13-14, pp. 2127 -2141, 2004.
     Google Scholar
  9. Y. Cheng-wen and Y. Jian, "Application of ann for the prediction of building energy consumption at different climate zones with hdd and cdd," in 2nd International Conference on Future Computer and Communication, 2010.
     Google Scholar
  10. P. González and J. Zamarreño, "Prediction of hourly energy consumption in buildings based on a feedback artificial neural network," Energy and Buildings, vol. 37, no. (6), pp. 595 -- 601, 2005.
     Google Scholar
  11. L. Kangji , S. Hongye and C. Jian , "Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study," Energy and Buildings, vol. 43, no. 10, p. 2893 – 2899, 2011.
     Google Scholar
  12. T. Olofsson, S. Andersson and R. Östin, "A method for predicting the annual building heating demand based on limited performance data," Energy and Buildings, vol. 28, no. 1, pp. 101-108, 1998.
     Google Scholar
  13. S. Karatasou, M. Santamouris and V. Geros, "Modeling and predicting building's energy use with artificial neural networks: Methods and results," Energy and Buildings, vol. 38, no. 8, pp. 949-958, 2006.
     Google Scholar
  14. S. Kalogirou, "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, vol. 1, no. 3, pp. 201-216, 2006.
     Google Scholar
  15. B. Dong, C. Cao and S. E. Lee, "Applying support vector machines to predict building energy consumption in tropical region," Energy and Buildings, vol. 37, no. 5, pp. 545-553, 2005.
     Google Scholar
  16. F. Lai, F. Magoules and F. Lherminier, "Vapnik's learning theory applied to energy consumption forecasts in residential buildings," International Journal of Computer Mathematics, vol. 85, no. 10, pp. 1563-1588, 2008.
     Google Scholar
  17. P.-F. Pai and W.-C. Hong, "Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms," Electric Power Systems Research, vol. 74, no. 3, pp. 101-108, 1998.
     Google Scholar
  18. Y. Fu, Z. Li, H. Zhang and P. Xu, "Using support vector machine to predict next day electricity load of public buildings with sub-metering devices," Procedia Engineering, vol. 121, pp. 1016-1022, 2015.
     Google Scholar
  19. J. Yang and J. Stenzel, "Short-term load forecasting with increment regression tree," Electric Power Systems Research, vol. 76, no. 9, pp. 880-888, 2006.
     Google Scholar
  20. G. K. Tso and K. K. Yau, "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, vol. 32, no. 9, pp. 1761-1768, 2007.
     Google Scholar
  21. A. Shabani, A. Paul, R. Platon and E. Hüllermeier, "Predicting the Electricity Consumption of Buildings: An Improved CBR Approach," in International Conference on Case-Based Reasoning, Atlanta, 2016.
     Google Scholar
  22. R. Platon, J. Martel and K. Zoghlami, "CBR model for predicting a building's electricity use: On-line implementation in the absence of historical data," in 23rd International Conference on Case-Based Reasoning, Frankfurt, 2015.
     Google Scholar
  23. H. X. Zhao and F. Magoulès, "Parallel support vector machines applied to the prediction of multiple buildings energy consumption," Journal of Algorithms and Computational Technology, vol. 4, no. 2, pp. 231-249, 2010.
     Google Scholar
  24. A. Shabani and O. Zavalani, "Predicting Building Energy Consumption using Engineering and Data Driven Approaches: A Review," European Journal of Engineering Research and Science, vol. 2, no. 5, pp. 44-49, 2017.
     Google Scholar
  25. Y. Yang, H. Rivard and R. Zmeureanu, "On-line building energy prediction using adaptive artificial neural networks," Energy and Buildings, vol. 37, pp. 1250-1259, 2005.
     Google Scholar
  26. S. Haykin, Neural networks: a comprehensive foundation, Prentice Hall, 2002.
     Google Scholar
  27. O. Zavalani, A. Spahiu and L. Dhamo, "Energy Efficiency as Clean Energy Solution," Special Edition on Advanced Technique of Estimation Applications in Electrical Engineering, June-2013.
     Google Scholar
  28. O.Zavalani, "Reducing energy in buildings by using energy management systems and alternative energy-saving systems," in . Energy Market (EEM), 8th International Conference. Publisher: IEEE, doi: 10.1109/EEM.2011.595, Zagreb, 2011.
     Google Scholar
  29. A. Shabani, O. Zavalani and D. Panxhi, "State of art identification and monitoring methods for electric loads in commercial and residential buildings," in 5th International Conference of Ecosystem, ICE 2015 - Essay on Ecosystems & Environmental Research, ICE 2015,, Tirana, 2015.
     Google Scholar
  30. Q. Li, Q. Meng, J. Cai, H. Yoshino and A. Mochida, "Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks," Energy Conversion and Management, vol. 5, no. 1, pp. 90-96, 2009.
     Google Scholar
  31. Q. LI, Q. Meng, J. Cai, H. Yoshin and A. Mochida, "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, vol. 86, no. 10, pp. 2249-2256, 2009.
     Google Scholar
  32. L. Xuemei, D. Lixing, L. Jinhu, X. Gang and L. Jibin, "A novel hybrid approach of kpca and svm for building cooling load prediction," in Knowledge Discovery and Data Mining, 2010. WKDD'10, 2010.
     Google Scholar
  33. L. Xuemei, D. Yuyan, D. Lixing and J. Liangzhong, "Building cooling load forecasting using fuzzy support vector machine and fuzzy c-mean clustering," in Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010.
     Google Scholar
  34. . H.-x. Zhao and F. Magoulès, "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, vol. 16, no. 6, pp. 3586-3592, 2012.
     Google Scholar
  35. A. Shabani, D. Panxhi and O. Zavalani, "Short term building energy consumption forecasting using artificial neural network model," in 6th International Conference of Ecosystems - Proceedings Book, Tirana, 2016/6.
     Google Scholar