Abstract
The study of energy efficiency in buildings is an active field of research. Modeling and prediction of power-related magnitudes allow us to analyse the electrical consumption. This can lead to environmental and economical benefits. In this study we compare different techniques to predict active power consumed by buildings of the University of León (Spain). The original dataset contains time, environmental and electric data for 30 buildings. In our study we follow a two-step procedure: first, we grouped the buildings in terms of their electric load using principal component analysis (PCA) and k-medoids based clustering. From this clustering we selected three prototype buildings. Second, we have applied neural network-based machine learning techniques to carry out the prediction task. In particular, we used well-known multi layer perceptron (MLP) and extreme learning machine (ELM) algorithms, as well as a new model proposed in this paper which constitutes its major contribution: random extreme learning machines (RELM). Our analysis shows that RELM advantages MLP and ELM in labour days, which is the most interested case because of it highest electric demand.
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Notes
1 toe = 1 tonne of oil equivalent = 11.63 MWh.
References
Alonso, S.: Supervisión de la energía eléctrica en edificios públicos de uso docente basada en técnicas de minería de datos visual. Ph.D. thesis, Departamento de Ingeniería Eléctrica, Electrónica, de Computadores y Sistemas. Universidad de Oviedo (2012)
Bian, X., Xu, Q., Li, B., Xu, L.: Equipment fault forecasting based on a two-level hierarchical model. In: 2007 IEEE International Conference on Automation and Logistics, pp. 2095–2099 (2007)
Bishop, C.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Carpinteiro, O., Alves da Silva, A., Feichas, C.: A hierarchical neural model in short-term load forecasting. In: IJCNN (6), pp. 241–248 (2000)
Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice Hall, USA (2009)
Huang, G., Zhu, Q., Siew, C.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)
I.E.A. International Energy Agency.: Energy performance certification of buildings (2013)
Jolliffe, I.: Principal Component Analysis. Springer, Berlin (1986)
Kourentzes, N., Barrow, D.K., Crone, S.F.: Neural network ensemble operators for time series forecasting. Expert Syst. Appl. 41(9), 4235–4244 (2014)
Kusiak, A., Li, M., Tang, F.: Modeling and optimization of HVAC energy consumption. Appl. Energy 87(10), 3092–3102 (2010)
Ma, Y., Yu, J., Yang, C., Wang, L.: Study on power energy consumption model for large-scale public building. In: Proceedings of the 2nd international workshop on intelligent systems and applications, pp. 1–4 (2010)
Mateo, F., Carrasco, J.J., Millán-Giraldo, M., Sellami, A., Escandell-Montero, P., Martínez-Martínez, J.M., Soria-Olivas, E.: Machine learning techniques for short-term electric power demand prediction. In: 21st European Symposium on Artificial Neural Networks, ESANN 2013, Bruges, Belgium, April 24–26 (2013)
Ministerio de Fomento, Gobierno de España: Código Técnico de la Edificación (2010). http://www.codigotecnico.org/images/stories/pdf/ahorroEnergia/DBHE.pdf
Paliwal, M., Kumar, U.: Neural networks and statistical techniques: a review of applications. Expert Syst. Appl. 36(1), 2–17 (2009)
Soliman, S., Al-Kandari, A. (eds.): Electric load modeling for long-term forecasting. In: Electrical Load Forecasting, pp. 353–406. Butterworth-Heinemann, Boston (2010)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic, USA (2006)
Unión Europea.: Directiva 2006/32/CE del Parlamento europeo y del Consejo de 5 de abril sobre la eficiencia del uso final de la energía y los servicios energéticos (2006)
Unión Europea.: Directiva 2012/27/UE del Parlamento europeo y del Consejo de 25 de octubre relativa a la eficiencia energéica (2012)
US Department of Energy.: Buildings Energy Data Book (2010)
Vellido, A., Lisboa, P., Vaughan, J.: Neural networks in business: a survey of applications (1992–1998). Expert Syst. Appl. 17(1), 51–70 (1999)
Vergara, G., Cózar, J., Romero-González, C., Gámez, J.A., Soria-Olivas, E.: Comparing elm against mlp for electrical power prediction in buildings. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo-Moreo, F.J., Adeli, H., editors. Bioinspired Computation in Artificial Systems, Lecture Notes in Computer Science, vol. 9108, pp. 409–418. Springer International Publishing (2015)
Willmott, C., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 30(1), 79 (2005)
Wong, S., Wan, K., Lam, T.: Artificial neural networks for energy analysis of office buildings with daylighting. Appl. Energy 87(2), 551–557 (2010)
Yu, H., Wilamowski, B.: The Industrial Electronics Handbook, vol. 5. CRC, Boca Raton (2011)
Zhao, H., Magoulès, F.: Parallel support vector machines applied to the prediction of multiple buildings energy consumption. J. Algorithms Comput. Technol. 4(2), 231–249 (2010)
Acknowledgments
This work has been partially supported with JCCM funds by means of the project PEII-2014-049-P. Authors want to thank the SUPRESS research group of the university of León for their collaboration.
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Vergara, G., Alonso-Barba, J.I., Soria-Olivas, E. et al. Random extreme learning machines to predict electric load in buildings. Prog Artif Intell 5, 129–135 (2016). https://doi.org/10.1007/s13748-015-0077-6
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DOI: https://doi.org/10.1007/s13748-015-0077-6