Development of surrogate models using artificial neural network for building shell energy labelling
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Jlm Jan Hensen | Ana Paula Melo | Roberto Lamberts | D Daniel Cóstola | R. Lamberts | D. Cóstola | J. Hensen | A. Melo
[1] Sebastian Herkel,et al. Design, monitoring and evaluation of a low energy office building with passive cooling by night ventilation , 2004 .
[2] Takashi Inoue,et al. Solar shading and daylighting by means of autonomous responsive dimming glass: Practical application , 2003 .
[3] Constantinos A. Balaras,et al. Data collection and analysis of the building stock and its energy performance—An example for Hellenic buildings , 2010 .
[4] Jlm Jan Hensen,et al. Teaching building performance simulation : some quality assurance issues and experiences , 2004 .
[5] Fu-Sheng Gao,et al. Night ventilation control strategies in office buildings , 2009 .
[6] Leidy E. Klotz,et al. Unintended anchors: Building rating systems and energy performance goals for U.S. buildings , 2010 .
[7] Geoffrey Van Moeseke,et al. Impact of control rules on the efficiency of shading devices and free cooling for office buildings , 2007 .
[8] A. Olsson,et al. On Latin hypercube sampling for structural reliability analysis , 2003 .
[9] Jin Yang,et al. On-line building energy prediction using adaptive artificial neural networks , 2005 .
[10] Dirk Saelens,et al. Energy and comfort performance of thermally activated building systems including occupant behavior , 2011 .
[11] Roberto Lamberts,et al. Development of envelope efficiency labels for commercial buildings: Effect of different variables on electricity consumption , 2008 .
[12] Jesús M. Zamarreño,et al. Prediction of hourly energy consumption in buildings based on a feedback artificial neural network , 2005 .
[13] Guilherme Carrilho da Graça,et al. Energy certification of existing office buildings: Analysis of two case studies and qualitative reflection , 2013 .
[14] Abdullatif Ben-Nakhi,et al. Architecture and performance of neural networks for efficient A/C control in buildings , 2003 .
[15] Sebastian Herkel,et al. Design of passive cooling by night ventilation: evaluation of a parametric model and building simulation with measurements , 2003 .
[16] Oliver Probst,et al. Cooling load of buildings and code compliance , 2004 .
[17] Jlm Jan Hensen,et al. Assessing the accuracy of a simplified building energy simulation model using BESTEST: The case study of Brazilian regulation , 2012 .
[18] A.W.M. van Schijndel,et al. Reducing peak requirements for cooling by using thermally activated building systems , 2010 .
[19] Tony N.T. Lam,et al. Artificial neural networks for energy analysis of office buildings with daylighting , 2010 .
[20] K.S.Y. Wan,et al. Representative building design and internal load patterns for modelling energy use in residential buildings in Hong Kong , 2004 .
[21] Mark Richard Wilby,et al. Setting up GHG-based energy efficiency targets in buildings: The Ecolabel , 2013 .
[22] Fernando Oscar Ruttkay Pereira,et al. Using artificial neural networks to predict the impact of daylighting on building final electric energy requirements , 2013 .
[23] Henk Visscher,et al. Barriers and opportunities for labels for highly energy-efficient houses , 2010 .
[24] Bernard Widrow,et al. 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.
[25] Richard de Dear,et al. Impact of different building ventilation modes on occupant expectations of the main IEQ factors , 2012 .
[26] Jian Chu,et al. Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A compa , 2011 .
[27] Eric Wai Ming Lee,et al. A study of the importance of occupancy to building cooling load in prediction by intelligent approach , 2011 .
[28] António E. Ruano,et al. Prediction of building's temperature using neural networks models , 2006 .
[29] Aris Tsangrassoulis,et al. On the cooling potential of night ventilation techniques in the urban environment , 2005 .
[30] Benjamin C. M. Fung,et al. A methodology for identifying and improving occupant behavior in residential buildings , 2011 .
[31] R. Dear,et al. Thermal adaptation in the built environment: a literature review , 1998 .
[32] Giovanni Zemella,et al. Optimised design of energy efficient building faades via Evolutionary Neural Networks , 2011 .
[33] Gian Vincenzo Fracastoro,et al. A methodology for assessing the energy performance of large scale building stocks and possible appli , 2011 .
[34] Sankar K. Pal,et al. Fuzzy models for pattern recognition : methods that search for structures in data , 1992 .
[35] M. Nasser,et al. Scenarios of application of energy certification procedure for residential buildings in Lebanon , 2007 .
[36] Viktor Dorer,et al. Application range of thermally activated building systems tabs , 2007 .
[37] Andrew H. Sung,et al. Ranking importance of input parameters of neural networks , 1998 .
[38] Zhu Han-qing,et al. The geological characteristics of Yijiahe geothermal resources in Yongxiu,Jiangxi , 2005 .
[39] Rasmus Lund Jensen,et al. Dynamic heat storage and cooling capacity of a concrete deck with PCM and thermally activated building system , 2012 .
[40] Roberto Battiti,et al. Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.
[41] Jlm Jan Hensen,et al. Some quality assurance issues and experiences in teaching building performance simulation , 2004 .
[42] Norbert Jankowski,et al. Survey of Neural Transfer Functions , 1999 .
[43] O. T. Masoso,et al. The dark side of occupants’ behaviour on building energy use , 2010 .
[44] Constantinos A. Balaras,et al. Energy performance of buildings—EPBD in Greece , 2012 .
[45] Paulo Santos,et al. Review of passive PCM latent heat thermal energy storage systems towards buildings’ energy efficiency , 2013 .
[46] Richard de Dear,et al. Combined thermal acceptability and air movement assessments in a hot humid climate , 2011 .
[47] Gail Brager,et al. Thermal comfort in naturally ventilated buildings: revisions to ASHRAE Standard 55 , 2002 .
[48] Abdullatif Ben-Nakhi,et al. Cooling load prediction for buildings using general regression neural networks , 2004 .
[49] J. Ravetz. State of the stock--What do we know about existing buildings and their future prospects? , 2008 .
[50] Michael Y. Hu,et al. Forecasting with artificial neural networks: The state of the art , 1997 .
[51] A. K. de Wit,et al. Hydronic circuit topologies for thermally activated building systems – design questions and case study , 2012 .
[52] Masayuki Ichinose,et al. Thermotropic glass with active dimming control for solar shading and daylighting , 2008 .
[53] M. D. McKay,et al. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .
[54] I A Basheer,et al. Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.
[55] Priyadarsini Rajagopalan,et al. Building energy efficiency labeling programme in Singapore , 2008 .
[56] Françoise Bartiaux,et al. Do homeowners use energy labels? A comparison between Denmark and Belgium , 2007 .
[57] Yong Wu,et al. A review of building energy efficiency in China during “Eleventh Five-Year Plan” period , 2012 .
[58] Georgios A. Florides,et al. The geothermal characteristics of the ground and the potential of using ground coupled heat pumps in , 2011 .
[59] Hongxing Yang,et al. Vertical-borehole ground-coupled heat pumps: A review of models and systems , 2010 .
[60] Martin Vraa Nielsen,et al. Quantifying the potential of automated dynamic solar shading in office buildings through integrated simulations of energy and daylight , 2011 .
[61] Niccolò Aste,et al. An Algorithm for Designing Dynamic Solar Shading System , 2012 .
[62] P Pieter-Jan Hoes,et al. Gebruikersgedrag in gebouwsimulaties - van eenvoudig tot geavanceerd gebruikersgedragmodel:een gevoeligheidsanalyse voor het gebruikersgedrag en een onderzoek naar de robuustheid van een gebouw voor de gebruiker , 2007 .
[63] P Pieter-Jan Hoes,et al. Investigating the potential of a novel low-energy house concept with hybrid adaptable thermal storage , 2011 .
[64] Jlm Jan Hensen,et al. Climate adaptive building shells: state-of-the-art and future challenges , 2013 .
[65] Ming Qu,et al. A review for the applications and integrated approaches of ground-coupled heat pump systems , 2011 .
[66] Ahmed Al-Salaymeh,et al. Influence of infiltration on the energy losses in residential buildings in Amman , 2012 .
[67] Agis M. Papadopoulos,et al. A typological classification of the Greek residential building stock , 2011 .
[68] Patxi Hernandez,et al. Development of a methodology for life cycle building energy ratings , 2011 .
[69] Giuliano Dall'O',et al. A methodology for the energy performance classification of residential building stock on an urban scale , 2012 .
[70] Godfried Augenbroe,et al. Analysis of uncertainty in building design evaluations and its implications , 2002 .
[71] Fariborz Haghighat,et al. Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .
[72] Roberto Lamberts,et al. Parameters and methods adopted in the energy efficiency labelling regulation for buildings: part 2: simulation method , 2010 .
[73] Chonggang Xu,et al. Abstract , 2001, Veterinary Record.
[74] Carlos E. Pedreira,et al. Neural networks for short-term load forecasting: a review and evaluation , 2001 .
[75] Roberto Lamberts,et al. Parameters and methods adopted in the energy eficiency regulation for buildings: part 1: prescriptive method , 2010 .
[76] Katashi Matsunawa,et al. A nomograph for estimating annual cooling and heating energy requirements in buildings dominated by internal loads , 1988 .
[77] Per Heiselberg,et al. Parameter study on performance of building cooling by night-time ventilation , 2008 .
[78] Robert John Lark,et al. Adaptable buildings: A systems approach , 2013 .
[79] Giorgio Baldinelli,et al. Double skin facades for warm climate regions : Analysis of a solution with an integrated movable shading system , 2009 .
[80] Amanda Pertzborn,et al. Effective design and operation of hybrid ground-source heat pumps: Three case studies , 2011 .
[81] Alberto Hernandez Neto,et al. Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .
[82] James E. McMahon,et al. Governments should implement energy-efficiency standards and labels--cautiously , 2003 .
[83] Betul Bektas Ekici,et al. Prediction of building energy consumption by using artificial neural networks , 2009, Adv. Eng. Softw..
[84] Baizhan Li,et al. A method of identifying and weighting indicators of energy efficiency assessment in Chinese residential buildings , 2010 .
[85] V. Geros,et al. Modeling and predicting building's energy use with artificial neural networks: Methods and results , 2006 .
[86] S. Kalaiselvam,et al. Sustainable thermal energy storage technologies for buildings: A review , 2012 .