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Parsimonious Modeling for Estimating Hospital Cooling Demand to Reduce Maintenance Costs and Power Consumption

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Hybrid Artificial Intelligent Systems (HAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11734))

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Abstract

Hospitals are massive consumers of energy, and their cooling systems for HVAC and sanitary uses are particularly energy-intensive. Forecasting the thermal cooling demand of a hospital facility is a remarkable method for its potential to improve the energy efficiency of these buildings. A predictive model can help forecast the activity of water-cooled generators and improve the overall efficiency of the whole system. Therefore, power generation can be adapted to the real demand expected and adjusted accordingly. In addition, the maintenance costs related to power-generator breakdowns or ineffective starts and stops can be reduced. This article details the steps taken to develop an optimal and efficient model based on a genetic methodology that searches for low-complexity models through feature selection, parameter tuning and parsimonious model selection. The methodology, called GAparsimony, has been tested with neural networks, support vector machines and gradient boosting techniques. This new operational method employed herein can be replicated in similar buildings with comparable water-cooled generators, regardless of whether the buildings are new or existing structures.

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References

  1. OECD/IEA: International energy agency (2014)

    Google Scholar 

  2. IDAE, Fenercom: Guía de ahorro y eficiencia energética en hospitales. Fenercom (2010)

    Google Scholar 

  3. Yoon, S.H., Kim, S.Y., Park, G.H., Kim, Y.K., Cho, C.H., Park, B.H.: Multiple power-based building energy management system for efficient management of building energy. Sustain. Cities Soc. 42, 462–470 (2018)

    Article  Google Scholar 

  4. Missaoui, R., Joumaa, H., Ploix, S., Bacha, S.: Managing energy smart homes according to energy prices: analysis of a building energy management system. Energy Build. 71, 155–167 (2014)

    Article  Google Scholar 

  5. Palme, M.: The possible shift between heating and cooling demand of buildings under climate change conditions: are some mitigation policies wrongly understood? In: Sayigh, A. (ed.) Mediterranean Green Buildings & Renewable Energy, pp. 417–422. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-30746-6_30

    Chapter  Google Scholar 

  6. Saeedi, M., Moradi, M., Hosseini, M., Emamifar, A., Ghadimi, N.: Robust optimization based optimal chiller loading under cooling demand uncertainty. Appl. Therm. Eng. 148, 1081–1091 (2019)

    Article  Google Scholar 

  7. Wang, L., Lee, E.W., Yuen, R.K.: Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach. Appl. Energy 228, 1740–1753 (2018)

    Article  Google Scholar 

  8. Abdel-Aal, R.: Modeling and forecasting electric daily peak loads using abductive networks. Int. J. Electr. Power Energy Syst. 28(2), 133–141 (2006)

    Article  Google Scholar 

  9. Chitsaz, H., Shaker, H., Zareipour, H., Wood, D., Amjady, N.: Short-term electricity load forecasting of buildings in microgrids. Energy Build. 99, 50–60 (2015)

    Article  Google Scholar 

  10. Shepero, M., van der Meer, D., Munkhammar, J., Widén, J.: Residential probabilistic load forecasting: a method using Gaussian process designed for electric load data. Appl. Energy 218, 159–172 (2018)

    Article  Google Scholar 

  11. Li, Y., Che, J., Yang, Y.: Subsampled support vector regression ensemble for short term electric load forecasting. Energy 164, 160–170 (2018)

    Article  Google Scholar 

  12. Yang, Y., Che, J., Deng, C., Li, L.: Sequential grid approach based support vector regression for short-term electric load forecasting. Appl. Energy 238, 1010–1021 (2019)

    Article  Google Scholar 

  13. Bagnasco, A., Fresi, F., Saviozzi, M., Silvestro, F., Vinci, A.: Electrical consumption forecasting in hospital facilities: an application case. Energy Buildings 103(Complete), 261–270 (2015)

    Article  Google Scholar 

  14. Jetcheva, J.G., Majidpour, M., Chen, W.P.: Neural network model ensembles for building-level electricity load forecasts. Energy Build. 84, 214–223 (2014)

    Article  Google Scholar 

  15. Hsu, Y.Y., Tung, T.T., Yeh, H.C., Lu, C.N.: Two-stage artificial neural network model for short-term load forecasting. IFAC-PapersOnLine 51(28), 678–683 (2018). 10th IFAC Symposium on Control of Power and Energy Systems CPES 2018

    Article  Google Scholar 

  16. Singh, P., Dwivedi, P., Kant, V.: A hybrid method based on neural network and improved environmental adaptation method using controlled gaussian mutation with real parameter for short-term load forecasting. Energy 174, 460–477 (2019)

    Article  Google Scholar 

  17. Avalos, M., Grandvalet, Y., Ambroise, C.: Parsimonious additive models. Comput. Stat. Data Anal. 51(6), 2851–2870 (2007)

    Article  MathSciNet  Google Scholar 

  18. Li, H., Shu, D., Zhang, Y., Yi, G.Y.: Simultaneous variable selection and estimation for multivariate multilevel longitudinal data with both continuous and binary responses. Comput. Stat. Data Anal. 118, 126–137 (2018)

    Article  MathSciNet  Google Scholar 

  19. Husain, H., Handel, N.: Automated machine learning. A paradigm shift that accelerates data scientist productivity, May 2017

    Google Scholar 

  20. Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems vol. 28, pp. 2962–2970. Curran Associates, Inc. (2015)

    Google Scholar 

  21. Sanz-Garcia, A., Fernandez-Ceniceros, J., Antonanzas-Torres, F., Pernia-Espinoza, A., Martinez-de Pison, F.J.: GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace. Appl. Soft Comput. 35, 13–28 (2015)

    Article  Google Scholar 

  22. Urraca, R., Sodupe-Ortega, E., Antonanzas, J., Antonanzas-Torres, F., de Pison, F.M.: Evaluation of a novel GA-based methodology for model structure selection: the GA-PARSIMONY. Neurocomputing 271(Supplement C), 9–17 (2018)

    Article  Google Scholar 

  23. Sanz-García, A., Fernández-Ceniceros, J., Antoñanzas-Torres, F., Martínez-de Pisón, F.J.: Parsimonious support vector machines modelling for set points in industrial processes based on genetic algorithm optimization. In: Herrero, Á., et al. (eds.) International Joint Conference SOCO13-CISIS13-ICEUTE13. Advances in Intelligent Systems and Computing, vol. 239, pp. 1–10. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-01854-6_1

    Chapter  Google Scholar 

  24. Urraca, R., Sanz-Garcia, A., Fernandez-Ceniceros, J., Sodupe-Ortega, E., Martinez-de-Pison, F.J.: Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 632–643. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19644-2_52

    Chapter  Google Scholar 

  25. Fernandez-Ceniceros, J., Sanz-Garcia, A., Antonanzas-Torres, F., de Pison, F.M.: A numerical-informational approach for characterising the ductile behaviour of the T-stub component. Part 2: Parsimonious soft-computing-based metamodel. Eng. Struct. 82, 249–260 (2015)

    Article  Google Scholar 

  26. Antonanzas-Torres, F., Urraca, R., Antonanzas, J., Fernandez-Ceniceros, J., de Pison, F.M.: Generation of daily global solar irradiation with support vector machines for regression. Energy Convers. Manag. 96, 277–286 (2015)

    Article  Google Scholar 

  27. Martinez-de-Pison, F.J., Fraile-Garcia, E., Ferreiro-Cabello, J., Gonzalez, R., Pernia, A.: Searching parsimonious solutions with GA-PARSIMONY and XGBoost in high-dimensional databases. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds.) ICEUTE/SOCO/CISIS -2016. AISC, vol. 527, pp. 201–210. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47364-2_20

    Chapter  Google Scholar 

  28. Sanz-Garcia, A., Fernandez-Ceniceros, J., Antonanzas-Torres, F., Pernia-Espinoza, A., Martinez-de Pison, F.: GA-parsimony. Appl. Soft Comput. 35(C), 13–28 (2015)

    Article  Google Scholar 

  29. Martínez-De-Pisón, F.J.: GAparsimony: GA-based optimization R package for searching accurate parsimonious models (2017). R package version 0.9-1

    Google Scholar 

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Authors and Affiliations

  1. EDMANS Group, Department of Mechanical Engineering, University of La Rioja, 26004, Logroño, Spain

    Eduardo Dulce & Francisco Javier Martinez-de-Pison

Authors
  1. Eduardo Dulce
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  2. Francisco Javier Martinez-de-Pison
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Correspondence to Francisco Javier Martinez-de-Pison .

Editor information

Editors and Affiliations

  1. University of León, León, Spain

    Hilde Pérez García

  2. University of León, León, Spain

    Lidia Sánchez González

  3. University of León, León, Spain

    Manuel Castejón Limas

  4. University of A Coruña, Ferrol, Spain

    Héctor Quintián Pardo

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado Rodríguez

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Dulce, E., Martinez-de-Pison, F.J. (2019). Parsimonious Modeling for Estimating Hospital Cooling Demand to Reduce Maintenance Costs and Power Consumption. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_16

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  • DOI: https://doi.org/10.1007/978-3-030-29859-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29858-6

  • Online ISBN: 978-3-030-29859-3

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