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Enclosure Optimization

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School Buildings Rehabilitation

Abstract

This chapter presents a methodology for the optimization of the insulation thickness of external walls and roofs. Its application in school buildings rehabilitation is tried. The methodology is presented and formulated. Objectives related to building’s energy efficiency, summer comfort and life cycle cost are combined. A sensitivity analysis is presented and an example case is used to test the methodology.

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References

  • Aydinalp, M., Ismet Ugursal, V., & Fung, A. S. (2004). Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks. Applied Energy, 79(2), 159–178.

    Article  Google Scholar 

  • Ben-Nakhi, A. E., & Mahmoud, M. A. (2004). Cooling load prediction for buildings using general regression neural networks. Energy Conversion and Management, 45(13), 2127–2141.

    Article  Google Scholar 

  • Boithias, F., el Mankibi, M., & Michel, P. (2012a). Generic multi-objective optimization method of indoor and envelope systems’ control. University “Politehnica” of Bucharest Scientific Bulletin, Series C Electrical Engineering, 74(1), 57–66.

    Google Scholar 

  • Boithias, F., el Mankibi, M., & Michel, P. (2012b). Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction. Building Simulation, 5(2), 95–106.

    Article  Google Scholar 

  • Calise, F. (2010). Thermoeconomic analysis and optimization of high efficiency solar heating and cooling systems for different Italian school buildings and climates. Energy and Buildings, 42(7), 992–1003.

    Article  Google Scholar 

  • Calise, F. (2012). High temperature solar heating and cooling systems for different mediterranean climates: Dynamic simulation and economic assessment. Applied Thermal Engineering, 32, 108–124.

    Article  Google Scholar 

  • Calise, F., D’Accadia, M. D., & Vanoli, L. (2011). Thermoeconomic optimization of solar heating and cooling systems. Energy Conversion and Management, 52(2), 1562–1573.

    Article  Google Scholar 

  • Catalina, T., Virgone, J., & Blanco, E. (2008). Development and validation of regression models to predict monthly heating demand for residential buildings. Energy and Buildings, 40(10), 1825–1832.

    Article  Google Scholar 

  • Chantrelle, F. P., Lahmidi, H., Keilholz, W., Mankibi, M. E., & Michel, P. (2011). Development of a multicriteria tool for optimizing the renovation of buildings. Applied Energy, 88(4), 1386–1394.

    Article  Google Scholar 

  • Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Chichester, England: Wiley.

    MATH  Google Scholar 

  • Dhar, A., Reddy, T. A., & Claridge, D. E. (1998). Modeling hourly energy use in commercial buildings with Fourier series functional forms. Journal of Solar Energy Engineering, 120(3), 217–223.

    Article  Google Scholar 

  • Diakaki, C., Grigoroudis, E., & Kolokotsa, D. (2008). Towards a multi-objective optimization approach for improving energy efficiency in buildings. Energy and Buildings, 40(9), 1747–1754.

    Article  Google Scholar 

  • Diakaki, C., Grigoroudis, E., Kabelis, N., Kolokotsa, D., Kalaitzakis, K., & Stavrakakis, G. (2010). A multi-objective decision model for the improvement of energy efficiency in buildings. Energy, 35(12), 5483–5496.

    Article  Google Scholar 

  • EPBD. (2002). O.J.o.t.E. Communities, Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the Energy Performance of Buildings, 2003.

    Google Scholar 

  • EPBD. (2010). O.J.o.t.E. Communities, Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the Energy Performance of Buildings (recast), 2010.

    Google Scholar 

  • Freire, R. Z., Oliveira, G. H. C., & Mendes, N. (2008). Development of regression equations for predicting energy and hygrothermal performance of buildings. Energy and Buildings, 40(5), 810–820.

    Article  Google Scholar 

  • Gossard, D., Lartigue, B., & Thellier, F. (2013). Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network. Energy and Buildings, 67, 253–260.

    Article  Google Scholar 

  • Guedes, M. C., Matias, L., & Santos, C. P. (2009). Thermal comfort criteria and building design: Field work in Portugal. Renewable Energy, 34(11), 2357–2361.

    Article  Google Scholar 

  • Gustafsson, S. I. (2000). Optimisation of insulation measures on existing buildings. Energy and Buildings, 33(1), 49–55.

    Article  MathSciNet  Google Scholar 

  • Hamdy, M., Hasan, A., & Siren, K. (2013). A multi-stage optimization method for cost-optimal and nearly-zero-energy building solutions in line with the EPBD-recast 2010. Energy and Buildings, 56, 189–203.

    Article  Google Scholar 

  • Hasan, A., Vuolle, M., & Sirén, K. (2008). Minimisation of life cycle cost of a detached house using combined simulation and optimization. Building and Environment, 43(12), 2022–2034.

    Article  Google Scholar 

  • Helton, J. C., & Davis, F. J. (2003). Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering and System Safety, 81, 23–69.

    Article  Google Scholar 

  • Karatasou, S., Santamouris, M., & Geros, V. (2006). Modeling and predicting building’s energy use with artificial neural networks: Methods and results. Energy and Buildings, 38(8), 949–958.

    Article  Google Scholar 

  • Kawashima, M., Dorgan, C. E., & Mitchell, J. (1995). Hourly thermal load prediction for the next 24 hours by ARIMA, EWMA, LR, and an ANN. ASHRAE Transactions, 101(1), 186–200.

    Google Scholar 

  • Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and System Safety, 91(9), 992–1007.

    Article  Google Scholar 

  • Kreider, X. A. W. (1991). Artificial neural network demonstration for automated generation of energy use predictors for commercial Buildings. ASHRAE Transactions, 97(2), 775–779.

    Google Scholar 

  • Kumar, R., Aggarwal, R. K., & Sharma, J. D. (2013). Energy analysis of a building using artificial neural network: A review. Energy and Buildings, 65, 352–358.

    Article  Google Scholar 

  • Kumbaroğlu, G., & Madlener, R. (2012). Evaluation of economically optimal retrofit investment options for energy savings in buildings. Energy and Buildings, 49, 327–334.

    Article  Google Scholar 

  • Magnier, L., & Haghighat, F. (2010). Multiobjective optimization of buildings design using TRNSYS simulations, genetic algorithm, and artificial neural networks. Building and Environment, 45(3), 739–746.

    Article  Google Scholar 

  • Marler, R. T., & Arora, J. (2010). The weighted sum method for multi-objective optimization: New insights. Structural and Multidisciplinary Optimization, 41(6), 853–862.

    Article  MATH  MathSciNet  Google Scholar 

  • Mateus, T., & Oliveira, A. C. (2009). Energy and economic analysis of an integrated solar absorption cooling and heating system in different building types and climates. Applied Energy, 86(6), 949–957.

    Article  Google Scholar 

  • Mechaqrane, A., & Zouak, M. (2004). A comparison of linear and neural network ARX models applied to a prediction of the indoor temperature of a building. Neural Computing and Applications, 13(1), 32–37.

    Article  Google Scholar 

  • Ochoa, C. E., Aries, M. B. C., van Loenen, E. J., & Hensen, J. L. M. (2012). Considerations on design optimization criteria for windows providing low energy consumption and high visual comfort. Applied Energy, 95, 238–245.

    Article  Google Scholar 

  • O’neill, P. J., Crawley, D. B., & Schliesing, J. S. (1991). Using regression equations to determine the relative importance of inputs to energy simulations tools. In: Building simulation’91, pp. 283–289. 20–22 Aug 1991. Sophia-Antipolis, Nice, France.

    Google Scholar 

  • Ozel, M. (2012). Cost analysis for optimum thicknesses and environmental impacts of different insulation materials. Energy and Buildings, 49, 552–559.

    Article  Google Scholar 

  • Ozel, M. (2013). Determination of optimum insulation thickness based on cooling transmission load for building walls in a hot climate. Energy Conversion and Management, 66, 106–114.

    Article  Google Scholar 

  • Santamouris, M., Mihalakakou, G., Patargias, P., Gaitani, N., Sfakianaki, K., Papaglastra, M., et al. (2007). Using intelligent clustering techniques to classify the energy performance of school buildings. Energy and Buildings, 39(1), 45–51.

    Article  Google Scholar 

  • Suga, K., Kato, S., & Hiyama, K. (2010). Structural analysis of Pareto-optimal solution sets for multi-objective optimization: An application to outer window design problems using multiple objective genetic algorithms. Building and Environment, 45(5), 1144–1152.

    Article  Google Scholar 

  • Tso, G. K. F., & Yau, K. K. W. (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32(9), 1761–1768.

    Article  Google Scholar 

  • Wright, J. A., Loosemore, H. A., & Farmani, R. (2002). Optimization of building thermal design and control by multi-criterion genetic algorithm. Energy and Buildings, 34(9), 959–972.

    Article  Google Scholar 

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Correspondence to Ricardo M. S. F. Almeida .

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Almeida, R.M.S.F., de Freitas, V.P., Delgado, J.M.P.Q. (2015). Enclosure Optimization. In: School Buildings Rehabilitation. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-15359-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-15359-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15358-2

  • Online ISBN: 978-3-319-15359-9

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