Abstract
The study of energy efficiency in buildings is an active field of research. Modelling and predicting energy related magnitudes leads to analyse electric power consumption and can achieve economical benefits. In this study, two machine learning techniques are applied to predict active power in buildings. The real data acquired corresponds to time, environmental and electrical data of 30 buildings belonging to the University of León (Spain). Firstly, we segmented buildings in terms of their energy consumption using principal component analysis. Afterwards we applied ELM and MLP methods to compare their performance. Models were studied for different variable selections. Our analysis shows that the MLP obtains the lowest error but also higher learning time than ELM.
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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-Verlag New York, Inc. (2006)
Carpinteiro, O., Alves da Silva, A., Feichas, C.: A hierarchical neural model in short-term load forecasting. In: IJCNN (6), pp. 241–248 (2000), http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.859403
Ekici, B., Aksoy, U.: Prediction of building energy consumption by using artificial neural networks. Advances in Engineering Software 40(5), 356–362 (2009)
Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice-Hall (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)
Kusiak, A., Li, M., Tang, F.: Modeling and optimization of HVAC energy consumption. Applied 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)
Ministerio de Fomento, Gobierno de España: Código Técnico de la Edificación (2010), http://www.codigotecnico.org
Newsham, G., Birt, B.: Building-level occupancy data to improve arima-based electricity use forecasts. In: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-efficiency in Building, BuildSys 2010, pp. 13–18. ACM (2010)
Paliwal, M., Kumar, U.: Neural networks and statistical techniques: A review of applications. Expert Systems with Applications 36(1), 2–17 (2009)
Soliman, S., Al-Kandari, A.: Electric load modeling for long-term forecasting. In: Electrical Load Forecasting, pp. 353–406. Butterworth-Heinemann, Boston (2010)
U.S. Department of Energy: Buildings Energy Data Book (2010), http://buildingsdatabook.eren.doe.gov/DataBooks.aspx
Vellido, A., Lisboa, P., Vaughan, J.: Neural networks in business: a survey of applications (1992–1998). Expert Systems with Applications 17(1), 51–70 (1999)
Vergara, G., Carrasco, J., Martínez-Gómez, J., Domínguez, M., Gámez, J., Soria-Olivas, E.: Machine learning models to forecast daily power consumption profiles in buildings. Journal of Electrical Power and Energy Systems (2014) (submitted)
Willmott, C., Matsuura, K.: Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate Research 30(1), 79 (2005)
Wong, S., Wan, K., Lam, T.: Artificial neural networks for energy analysis of office buildings with daylighting. Applied Energy 87(2), 551–557 (2010)
Yu, H., Wilamowski, B.: The Industrial Electronics Handbook, vol. 5. CRC (2011)
Zhao, H., Magoulès, F.: Parallel support vector machines applied to the prediction of multiple buildings energy consumption. Journal of Algorithms & Computational Technology 4(2), 231–249 (2010)
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Vergara, G., Cózar, J., Romero-González, C., Gámez, J.A., Soria-Olivas, E. (2015). Comparing ELM Against MLP for Electrical Power Prediction in Buildings. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_43
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DOI: https://doi.org/10.1007/978-3-319-18833-1_43
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