Using artificial neural networks to assess HVAC related energy saving in retrofitted office buildings
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[1] Priyadarsini Rajagopalan,et al. Building energy efficiency labeling programme in Singapore , 2008 .
[2] Sebastian Herkel,et al. Energy efficient office buildings with passive cooling - Results and experiences from a research and demonstration programme , 2007 .
[3] R. Urraca,et al. Review of photovoltaic power forecasting , 2016 .
[4] William Chung,et al. Benchmarking the energy efficiency of commercial buildings , 2006 .
[5] M. D. Mainar-Toledo,et al. Multiple regression models to predict the annual energy consumption in the Spanish banking sector , 2012 .
[6] Jojo S.M. Li. A study of energy performance and efficiency improvement procedures of Government Offices in Hong Kong Special Administrative Region , 2008 .
[7] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[8] Eric Martinot. Energy efficiency and renewable energy in Russia , 1995 .
[9] Edward Vine,et al. The evolution of the US energy service company (ESCO) industry: from ESCO to Super ESCO , 1999 .
[10] Piljae Im,et al. Comparison of building energy use data between the United States and China , 2014, Energy and Buildings.
[11] Chirag Deb,et al. Determining key variables influencing energy consumption in office buildings through cluster analysis of pre- and post-retrofit building data , 2018 .
[12] M. Santamouris,et al. On the impact of urban climate on the energy consumption of buildings , 2001 .
[13] Mary Ann Piette,et al. Energy retrofit analysis toolkits for commercial buildings: A review , 2015 .
[14] Ali M. Malkawi,et al. A new methodology for building energy performance benchmarking: An approach based on intelligent clustering algorithm , 2014 .
[15] Eric Hirst,et al. Analysis of engineering audits at state-owned buildings in Minnesota , 1979 .
[16] Nedyomukti Imam Syafii,et al. Evaluation of the impact of the surrounding urban morphology on building energy consumption , 2011 .
[17] Le Yang,et al. Data and analytics to inform energy retrofit of high performance buildings , 2014 .
[18] Paul Cooper,et al. Existing building retrofits: Methodology and state-of-the-art , 2012 .
[19] Frederic Magoules,et al. Feature selection for support vector regression in the application of building energy prediction , 2011, 2011 IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI).
[20] Yeonsook Heo,et al. Calibration of building energy models for retrofit analysis under uncertainty , 2012 .
[21] F. Ascione. Energy conservation and renewable technologies for buildings to face the impact of the climate change and minimize the use of cooling , 2017 .
[22] S. D. Probert. UK governments' energy policies , 1987 .
[23] Ian Cooper. Government policy and managerial practices for conserving energy in non-domestic premises , 1982 .
[24] John Burnett,et al. A study of energy performance of hotel buildings in Hong Kong , 2000 .
[25] A. Ghanbarzadeh,et al. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data , 2010 .
[26] A. Pisello. State of the art on the development of cool coatings for buildings and cities , 2017 .
[27] Andrea Gasparella,et al. Energy audit of schools by means of cluster analysis , 2015 .
[28] Rehan Sadiq,et al. Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings , 2016 .
[29] Constantinos A. Balaras,et al. Energy conservation and retrofitting potential in Hellenic hotels , 1996 .
[30] Fan Zhang,et al. A review on time series forecasting techniques for building energy consumption , 2017 .
[31] Eric Hirst,et al. Energy use in Minnesota State-owned facilities , 1981 .
[32] Joseph C. Lam,et al. Multiple regression models for energy use in air-conditioned office buildings in different climates , 2010 .
[33] Howard Ross,et al. Building energy use compilation and analysis (BECA). Part C: Conservation progress in retrofitted commercial buildings , 1983 .
[34] V. I. Hanby,et al. Regression models for predicting UK office building energy consumption from heating and cooling demands , 2013 .
[35] Chirag Deb,et al. Forecasting Energy Consumption of Institutional Buildings in Singapore , 2015 .
[36] Fan Zhang,et al. Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique , 2016 .
[37] D. Kolokotsa,et al. Virtual Building Dataset for energy and indoor thermal comfort benchmarking of office buildings in Greece , 2009 .
[38] Liu Yang,et al. Sensitivity analysis and energy conservation measures implications , 2008 .
[39] Claude-Alain Roulet,et al. Elaboration of retrofit scenarios , 2002 .
[40] Samantha Hall,et al. Development and initial trial of a tool to enable improved energy & human performance in existing commercial buildings , 2014 .
[41] Chirag Deb,et al. Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks , 2016 .
[42] Elias B. Kosmatopoulos,et al. A roadmap towards intelligent net zero- and positive-energy buildings , 2011 .
[43] Maria Kolokotroni,et al. A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London , 2010 .
[44] Arun Kumar,et al. A review on modeling and simulation of building energy systems , 2016 .