Suitability analysis of modeling and assessment approaches in energy efficiency in buildings
暂无分享,去创建一个
[1] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[2] 新 雅夫,et al. ASHRAE(American Society of Heating,Refrigerating and Air-Conditioning Engineers)大会"国際年"行事に参加して , 1975 .
[3] Ryohei Yokoyama,et al. Sensitivity analysis in structure optimization of energy supply systems for a hospital , 2007 .
[4] Kelvin K. W. Yau,et al. Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .
[5] Fredrik Karlsson,et al. Measured and predicted energy demand of a low energy building: important aspects when using Building Energy Simulation , 2007 .
[6] Joseph Virgone,et al. Development and validation of regression models to predict monthly heating demand for residential buildings , 2008 .
[7] Jacob H. Stang,et al. Load prediction method for heat and electricity demand in buildings for the purpose of planning for mixed energy distribution systems , 2008 .
[8] Alberto Hernandez Neto,et al. Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .
[9] V. Ismet Ugursal,et al. Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .
[11] Nelson Fumo,et al. Methodology to estimate building energy consumption using EnergyPlus Benchmark Models , 2010 .
[12] Benjamin C. M. Fung,et al. A decision tree method for building energy demand modeling , 2010 .
[13] Paul Raftery,et al. Calibrating whole building energy models: An evidence-based methodology , 2011 .
[14] Paul Raftery,et al. Calibrating whole building energy models: Detailed case study using hourly measured data , 2011 .
[15] Nelson Fumo,et al. Robustness of a methodology for estimating hourly energy consumption of buildings using monthly util , 2011 .
[16] Jian Chu,et al. Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A compa , 2011 .
[17] Paul Raftery,et al. CALIBRATION OF A DETAILED BES MODEL TO MEASURED DATA USING AN EVIDENCE-BASED ANALYTICAL OPTIMISATION APPROACH , 2011 .
[18] Lynne E. Parker,et al. Energy and Buildings , 2012 .
[19] Ilaria Ballarini,et al. Analysis of the building energy balance to investigate the effect of thermal insulation in summer conditions , 2012 .
[20] Fu Xiao,et al. Quantitative energy performance assessment methods for existing buildings , 2012 .
[21] Pedro J. Mago,et al. Building hourly thermal load prediction using an indexed ARX model , 2012 .
[22] Qinglin Meng,et al. An integrated simulation method for building energy performance assessment in urban environments , 2012 .
[23] Vincenc Butala,et al. Analysis of building electric energy consumption data using an improved cooling degree day method , 2012 .
[24] Yeonsook Heo,et al. Calibration of building energy models for retrofit analysis under uncertainty , 2012 .
[25] Athanasios Tsanas,et al. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools , 2012 .
[26] Paul Raftery,et al. CALIBRATION OF WHOLE BUILDING ENERGY SIMULATION MODELS: DETAILED CASE STUDY OF A NATURALLY VENTILATED BUILDING USING HOURLY MEASURED DATA , 2012 .
[27] Henrik Brohus,et al. Quantification of uncertainty in predicting building energy consumption: A stochastic approach , 2012 .
[28] Velimir Čongradac,et al. Assessing the energy consumption for heating and cooling in hospitals , 2012 .
[29] Fu Xiao,et al. A simplified energy performance assessment method for existing buildings based on energy bill disaggregation , 2012 .
[30] Giuliano Dall'O',et al. Comparison between predicted and actual energy performance for summer cooling in high-performance residential buildings in the Lombardy region (Italy) , 2012 .
[31] Xiufeng Pang,et al. Uncertainties in Energy Consumption Introduced by Building Operations and Weather for a Medium-Size Office Building , 2012 .
[32] B. L. Gowreesunker,et al. Improved simulation of phase change processes in applications where conduction is the dominant heat transfer mode , 2012 .
[33] Mani Golparvar-Fard,et al. EPAR: Energy Performance Augmented Reality models for identification of building energy performance deviations between actual measurements and simulation results , 2013 .
[34] Sylvain Robert,et al. State of the art in building modelling and energy performances prediction: A review , 2013 .
[35] Youngjib Ham,et al. An automated vision-based method for rapid 3D energy performance modeling of existing buildings using thermal and digital imagery , 2013, Adv. Eng. Informatics.
[36] Tiberiu Catalina,et al. Multiple regression model for fast prediction of the heating energy demand , 2013 .
[37] Henk Visscher,et al. Actual and theoretical gas consumption in Dutch dwellings: What causes the differences? , 2013 .
[38] Luca Ferrarini,et al. Modeling and control of thermal energy of a large commercial building , 2013, 2013 IEEE International Workshop on Inteligent Energy Systems (IWIES).
[39] Nelson Fumo,et al. A review on the basics of building energy estimation , 2014 .
[40] Mohammad Mottahedi,et al. On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design , 2014 .
[41] Jie Zhao,et al. Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining , 2014 .
[42] Piotr Michalak,et al. The simple hourly method of EN ISO 13790 standard in Matlab/Simulink: A comparative study for the climatic conditions of Poland , 2014 .
[43] Paul Raftery,et al. A review of methods to match building energy simulation models to measured data , 2014 .
[44] Kevin R Grosskopf,et al. Benchmarking energy performance of building envelopes through a selective residual-clustering approach using high dimensional dataset , 2014 .
[45] Mani Golparvar-Fard,et al. Automated Diagnostics and Visualization of Potential Energy Performance Problems in Existing Buildings Using Energy Performance Augmented Reality Models , 2014, J. Comput. Civ. Eng..
[46] Andrea Costa,et al. Model calibration for building energy efficiency simulation , 2014 .
[47] Luca Evangelisti,et al. Building energy performance analysis: A case study , 2015 .
[48] Tianzhen Hong,et al. Occupant behavior modeling for building performance simulation: Current state and future challenges , 2015 .
[49] Luca Ferrarini,et al. Temperature Control of a Commercial Building With Model Predictive Control Techniques , 2015, IEEE Transactions on Industrial Electronics.
[50] Andrea Gasparella,et al. Calibrating historic building energy models to hourly indoor air and surface temperatures: Methodology and case study , 2015 .
[51] V. R. Dehkordi,et al. Hourly prediction of a building's electricity consumption using case-based reasoning, artificial neural networks and principal component analysis , 2015 .
[52] Zhu Neng,et al. An improved office building cooling load prediction model based on multivariable linear regression , 2015 .
[53] Federico Silvestro,et al. Electrical consumption forecasting in hospital facilities: An application case , 2015 .
[54] M. A. Rafe Biswas,et al. Regression analysis for prediction of residential energy consumption , 2015 .
[55] Henk Visscher,et al. Statistical model of the heating prediction gap in Dutch dwellings: Relative importance of building, household and behavioural characteristics , 2015 .
[56] Tony Roskilly,et al. This Work Is Licensed under a Creative Commons Attribution 4.0 International License Royapoor M, Roskilly T. Building Model Calibration Using Energy and Environmental Data. Energy and Buildings Building Model Calibration Using Energy and Environmental Data Keywords: Model Calibration Measured Energy , 2022 .
[57] Mani Golparvar-Fard,et al. Three-Dimensional Thermography-Based Method for Cost-Benefit Analysis of Energy Efficiency Building Envelope Retrofits , 2015, J. Comput. Civ. Eng..
[58] Taehoon Hong,et al. A dynamic energy performance curve for evaluating the historical trends in the energy performance of existing buildings using a simplified case-based reasoning approach , 2015 .
[59] Amin Esmaeili,et al. Scope for energy improvement for hospital imaging services in the USA , 2015, Journal of health services research & policy.
[60] Tianzhen Hong,et al. Occupancy schedules learning process through a data mining framework , 2015 .
[61] Paul S. Fischbeck,et al. Virtual home energy auditing at scale: Predicting residential energy efficiency using publicly available data , 2015 .
[62] Leonardo Vanneschi,et al. Prediction of energy performance of residential buildings: a genetic programming approach , 2015 .
[63] Tao Lu,et al. Modeling and forecasting energy consumption for heterogeneous buildings using a physical -statistical approach , 2015 .
[64] I. Santiago,et al. Stochastic model for lighting's electricity consumption in the residential sector. Impact of energy saving actions , 2015 .
[65] Shideh Shams Amiri,et al. Using multiple regression analysis to develop energy consumption indicators for commercial buildings in the U.S. , 2015 .
[66] Josiah L. Munda,et al. Residential lighting load profile modelling , 2015 .
[67] Farrokh Janabi-Sharifi,et al. Black-box modeling of residential HVAC system and comparison of gray-box and black-box modeling methods , 2015 .
[68] Radiša Jovanović,et al. Ensemble of various neural networks for prediction of heating energy consumption , 2015 .
[69] Martin Kaltschmitt,et al. Electricity consumption of medical plug loads in hospital laboratories: Identification, evaluation, prediction and verification , 2015 .
[70] Tianzhen Hong,et al. Data analysis and stochastic modeling of lighting energy use in large office buildings in China , 2015 .
[71] Sotiris Papantoniou,et al. Prediction of outdoor air temperature using neural networks: Application in 4 European cities , 2016 .
[72] Youngdeok Hwang,et al. Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings , 2016 .
[73] G. Fitzgerald,et al. 'I. , 2019, Australian journal of primary health.