Part of the SpringerBriefs in Energy book series (BRIEFSENERGY)


Energy efficiency in the building sector accounts for around 30–40% of the total energy consumption of human activities as per diverse sources (Pérez-Lombard et al. 2008; UNEP 2012). In 2010, its absolute consumption was 23.7 PWh and the International Energy Agency indicates that it can reach 38.4 PWh in 2040 (IEA 2013), being responsible for 38% of the greenhouse gas emissions (UNEP 2012). Around the world, this sector currently represents 13% of the GDP and it is expected that it increases to 15% in 2020 (Global Construction Perspectives and Oxford Economics 2013). Its total budget sat at 8.2 trillion dollars in 2013 (IHS Economics 2013) and it is foreseen that this will grow to 15 trillion dollars in 2025. As such, those strategies that focused on energy efficiency, consumption and emission reduction are one of the main challenges of the construction sector. Thus, the need of predicting these factors has forced official entities, like the European Union since 2002 (European Commission 2002), to obligatorily establish the measuring of buildings’ energy efficiency.


  1. BIP (2015) Banco Integrado de Proyectos. Ministerio de Desarrollo Social, Chile. Accessed 20 Feb 2016
  2. Boithias F, El Mankibi M, Michel P (2012) Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction. Build Simul 5:95–106. Scholar
  3. CORFO Chile (2016) Relevante Iniciativa para Potenciar la Productividad en Infraestructura. Accessed 21 Nov 2016
  4. Cui C, Wu T, Hu M et al (2016) Short-term building energy model recommendation system: a meta-learning approach. Appl Energy 172:251–263. Scholar
  5. Dall’O’ G, Sarto L, Sanna N et al (2015) On the use of an energy certification database to create indicators for energy planning purposes: application in northern Italy. Energy Policy 85:207–217. Scholar
  6. Deb C, Eang LS, Yang J, Santamouris M (2016) Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks. Energy Build 121:284–297. Scholar
  7. Dimoudi A, Tompa C (2008) Energy and environmental indicators related to construction of office buildings. Resour Conserv Recycl 53:86–95. Scholar
  8. Dowd RM, Mourshed M (2015) Low carbon buildings: sensitivity of thermal properties of opaque envelope construction and glazing. Energy Procedia 75:1284–1289. Scholar
  9. IHS Economics (2013) Global construction outlook: executive outlookGoogle Scholar
  10. Energy Agency I CO2 Emissions From Fuel Combustion Highlights 2015Google Scholar
  11. ERCROS (2014) Informe anual 2014. Ercros 67.
  12. Eriksen S, Aldunce P, Bahinipati CS et al (2011) When not every response to climate change is a good one: identifying principles for sustainable adaptation. Clim Dev 3:7–20. Scholar
  13. Estándares de Construcción con Criterios de Sustentabilidad (2016) Estándares de Construcción con Criterios de Sustentabilidad. SantiagoGoogle Scholar
  14. European Commission (2002) Directive 2002/91/EC of the European Parliament and of the council of 16 December 2002 on the energy performance of buildings. Off J Eur Union 65–71.
  15. European Commission (2010) Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings. Off J Eur Union 13–35.
  16. Gangolells M, Casals M (2012) Resilience to increasing temperatures: residential building stock adaptation through codes and standards. Build Res Inf 40:1–20. Scholar
  17. Global Construction Perspectives and Oxford Economics (2013) Global construction 2025Google Scholar
  18. Gong X, Akashi Y, Sumiyoshi D (2012) Optimization of passive design measures for residential buildings in different Chinese areas. Build Environ 58:46–57. Scholar
  19. González PA, Zamarreño JM (2005) Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy Build 37:595–601. Scholar
  20. Guan L (2009) Preparation of future weather data to study the impact of climate change on buildings. Build Environ 44:793–800. Scholar
  21. IEA (2013) World energy outlook 2013Google Scholar
  22. Ihm P, Krarti M (2012) Design optimization of energy efficient residential buildings in Tunisia. Build Environ 58:81–90. Scholar
  23. INN (2008) NCh 1079. Of 2008 Arquitectura y Construcción-Zonificación climático habitacional para ChileGoogle Scholar
  24. IPCC Data Distribution Centre. Accessed 15 Feb 2016
  25. IPCC (2014) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate changeGoogle Scholar
  26. ISO (2008) EN ISO 13790: 2008 Energy performance of buildings-Calculation of energy use for space heating and cooling. 3190–200Google Scholar
  27. Jentsch MF, Bahaj AS, James PAB (2008) Climate change future proofing of buildings—generation and assessment of building simulation weather files. Energy Build 40:2148–2168. Scholar
  28. Jentsch MF, James PAB, Bourikas L, Bahaj AS (2013) Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates. Renew Energy 55:514–524. Scholar
  29. Jeong Y-S, Lee S-E, Huh J-H (2012) Estimation of CO2 emission of apartment buildings due to major construction materials in the Republic of Korea. Energy Build 49:437–442. Scholar
  30. Jokisalo J, Kurnitski J (2007) Performance of EN ISO 13790 utilisation factor heat demand calculation method in a cold climate. Energy Build 39:236–247. Scholar
  31. Jurado S, Nebot À, Mugica F, Avellana N (2015) Hybrid methodologies for electricity load forecasting: entropy-based feature selection with machine learning and soft computing techniques. Energy 86:276–291. Scholar
  32. Kalvelage K, Passe U, Rabideau S, Takle ES (2014) Changing climate: the effects on energy demand and human comfort. Energy Build 76:373–380. Scholar
  33. Karatasou S, Santamouris M, Geros V (2006) Modeling and predicting building’s energy use with artificial neural networks: methods and results. Energy Build 38:949–958. Scholar
  34. Khayatian F, Sarto L, Dall’O’ G (2016) Application of neural networks for evaluating energy performance certificates of residential buildings. Energy Build 125:45–54. Scholar
  35. Kialashaki A, Reisel JR (2013) Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks. Appl Energy 108:271–280. Scholar
  36. Kljajić M, Gvozdenac D, Vukmirović S (2012) Use of neural networks for modeling and predicting boiler’s operating performance. Energy 45:304–311. Scholar
  37. Korolija I, Marjanovic-Halburd L, Zhang Y, Hanby VI (2013a) UK office buildings archetypal model as methodological approach in development of regression models for predicting building energy consumption from heating and cooling demands. Energy Build 60:152–162. Scholar
  38. Korolija I, Zhang Y, Marjanovic-Halburd L, Hanby VI (2013b) Regression models for predicting UK office building energy consumption from heating and cooling demands. Energy Build 59:214–227CrossRefGoogle Scholar
  39. Kumar R, Aggarwal RK, Sharma JD (2013) Energy analysis of a building using artificial neural network: a review. Energy Build 65:352–358. Scholar
  40. Li X, Wen J, Bai EW (2016) Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification. Appl Energy 164:69–88. Scholar
  41. M de OP (MOP) (2011) TDRe: Términos de Referencia Estandarizados con Parámetros de Eficiencia Energética y Confort Ambiental, para Licitaciones de Diseño y Obra de la Dirección de Arquitetura, Según Zonas Geográficas del País y Según Tipología de Edificios. Santiago, ChileGoogle Scholar
  42. Macas M, Moretti F, Fonti A et al (2016) The role of data sample size and dimensionality in neural network based forecasting of building heating related variables. Energy Build 111:299–310. Scholar
  43. Mba L, Meukam P, Kemajou A (2016) Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region. Energy Build 121:32–42. Scholar
  44. Mena R, Rodríguez F, Castilla M, Arahal MR (2014) A prediction model based on neural networks for the energy consumption of a bioclimatic building. Energy Build 82:142–155. Scholar
  45. Ministerio del Medio Ambiente Gobierno de Chile (2011) Cambio climático. Informe del Estado del Medio Ambiente 2011:427–463Google Scholar
  46. Mylona A (2012) The use of UKCP09 to produce weather files for building simulation. Build Serv Eng Res Technol 33:51–62. Scholar
  47. NCh835 (2007) Acondicionamiento térmico-Envolvente térmica de edificios-Cálculo de resistencias y transmitancias térmicas. NCh853:24Google Scholar
  48. Negendahl K (2015) Automation in Construction Building performance simulation in the early design stage: an introduction to integrated dynamic models. Autom Constr 54:39–53. Scholar
  49. Negendahl K, Nielsen TR (2015) Building energy optimization in the early design stages: a simplified method. Energy Build 105:88–99. Scholar
  50. Neto AH, Fiorelli FAS (2008) Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy Build 40:2169–2176. Scholar
  51. Overgaard S (2008) Issue paper: definition of primary and secondary energy. Prepared as input to Chapter 3: Standard International Energy Classification (SIEC) in the International Recommendation on Energy Statistics (IRES). Accessed 5 Sep 2016
  52. Parasonis J, Keizikas A, Kalibatiene D (2012) The relationship between the shape of a building and its energy performance. Archit Eng Des Manag 8:246–256. Scholar
  53. Pérez-Lombard L, Ortiz J, Pout C (2008) A review on buildings energy consumption information. Energy Build 40:394–398. Scholar
  54. Pulido-Arcas JA, Pérez-Fargallo A, Rubio-Bellido C (2016) Multivariable regression analysis to assess energy consumption and CO2 emissions in the early stages of offices design in Chile. Energy Build 133:738–753. Scholar
  55. Robert A, Kummert M (2012) Designing net-zero energy buildings for the future climate, not for the past. Build Environ 55:150–158. Scholar
  56. Rodger JA (2014) A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings. Expert Syst Appl 41:1813–1829CrossRefGoogle Scholar
  57. Ruano AE, Crispim EM, Conceição EZE, Lúcio MMJR (2006) Prediction of building’s temperature using neural networks models. Energy Build 38:682–694. Scholar
  58. Sorrell S (2015) Reducing energy demand: a review of issues, challenges and approaches. Renew Sustain Energy Rev 47:74–82. Scholar
  59. UNEP (2012) building design and construction: forging resource efficiency and sustainable developmentGoogle Scholar
  60. Wang H, Chen Q (2014) Impact of climate change heating and cooling energy use in buildings in the United States. Energy Build 82:428–436. Scholar
  61. Wang S, Yan C, Xiao F (2012) Quantitative energy performance assessment methods for existing buildings. Energy Build 55:873–888. Scholar
  62. Wong SL, Wan KKW, Lam TNT (2010) Artificial Neural Networks for energy analysis of office buildings with daylighting. Appl Energy 87:551–557. Scholar
  63. Yang J, Rivard H, Zmeureanu R (2005) On-line building energy prediction using adaptive artificial neural networks. Energy Build 37:1250–1259. Scholar
  64. Yang Q, Liu M, Shu C et al (2015) Impact analysis of window-wall ratio on heating and cooling energy consumption of residential buildings in hot summer and cold winter zone in China. 2015Google Scholar
  65. Zhao H, Magoulès F (2012) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16:3586–3592CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Higher Technical School of Building EngineeringUniversidad de SevillaSevilleSpain
  2. 2.Faculty of Construction, Architecture and DesignUniversidad Del Bío-BíoConcepción, VIII–ConcepciónChile

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