Towards fast energy performance evaluation: A pilot study for office buildings

Abstract Given the growing concern about building energy efficiency and the difficulty in applying complex simulation tools during retrofit practices, the need for easily and quickly estimating the building energy performance becomes pressing. As a pilot test, this study proposes a systematic method to develop a model, which can immediately assess the annual electricity consumption for office buildings with fan coil system in Shanghai. First, a base-case building model is established by EnergyPlus to create a pool of candidate inputs using orthogonal experiment design. Then, analysis of variance is used to identify a total of 10 key building design parameters, which are selected as the input variables in the support vector regression (SVR) model based on a well-structured database. The performance of SVR is optimized using genetic algorithm (GA) based on radial basis function kernel. Finally, two real office buildings in Shanghai with reliable measured data serve to evaluate the developed hybrid model. The resulting differences between the predicted and measured values are generally within 10%. It is expected that the developed database and model can be used to assess the likely energy savings/penalty related with certain parameter changes to some extent during the retrofit process for office buildings.

[1]  Leslie K. Norford,et al.  Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems , 2003 .

[2]  Jack P. C. Kleijnen,et al.  Design and Analysis of Monte Carlo Experiments , 2004 .

[3]  Paul Raftery,et al.  A review of methods to match building energy simulation models to measured data , 2014 .

[4]  Sang Hoon Lee,et al.  Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance , 2015 .

[5]  Joseph C. Lam,et al.  Sensitivity analysis of energy performance of office buildings , 1996 .

[6]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[7]  Godfried Augenbroe,et al.  Analysis of uncertainty in building design evaluations and its implications , 2002 .

[8]  Andrea Saltelli,et al.  Sensitivity Analysis for Importance Assessment , 2002, Risk analysis : an official publication of the Society for Risk Analysis.

[9]  Shengwei Wang,et al.  Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .

[10]  Navid Freidoonimehr,et al.  Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm , 2013 .

[11]  Joseph C. Lam,et al.  Multiple regression models for energy use in air-conditioned office buildings in different climates , 2010 .

[12]  Siaw Kiang Chou,et al.  Development of an energy‐estimating equation for large commercial buildings , 1993 .

[13]  Sylvain Robert,et al.  State of the art in building modelling and energy performances prediction: A review , 2013 .

[14]  T. R. Bement,et al.  Taguchi techniques for quality engineering , 1995 .

[15]  Sheng-wei Fei,et al.  Fault diagnosis of power transformer based on support vector machine with genetic algorithm , 2009, Expert Syst. Appl..

[16]  Fredrik Karlsson,et al.  Measured and predicted energy demand of a low energy building: important aspects when using Building Energy Simulation , 2007 .

[17]  Tianzhen Hong,et al.  Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration , 2014, Building and Environment.

[18]  Mohammad Mehdi Rashidi,et al.  Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network , 2011 .

[19]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[20]  Jan Carmeliet,et al.  CONSERVATIVE MODELLING OF THE MOISTURE AND HEAT TRANSFER IN BUILDING COMPONENTS UNDER ATMOSPHERIC EXCITATION , 2007 .

[21]  Paul Raftery,et al.  Calibrating whole building energy models: An evidence-based methodology , 2011 .

[22]  Jiejin Cai,et al.  Applying support vector machine to predict hourly cooling load in the building , 2009 .

[23]  Mohammad Mehdi Rashidi,et al.  Analysis and optimization of a transcritical power cycle with regenerator using artificial neural networks and genetic algorithms , 2011 .

[24]  Ping-Feng Pai,et al.  Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms , 2005 .

[25]  Hae Jin Kang,et al.  A Development of Heating and Cooling Load Prediction Equations for Office Buildings in Korea , 2014 .

[26]  Kadir Kavaklioglu,et al.  Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression , 2011 .

[27]  Joseph C. Lam,et al.  Regression analysis of high-rise fully air-conditioned office buildings , 1997 .

[28]  R. Sullivan,et al.  COMMERCIAL BUILDING ENERGY PERFORMANCE ANALYSIS USING MULTIPLE REGRESSION PROCEDURES , 1983 .

[29]  Ivan Vera,et al.  Energy indicators for sustainable development , 2007 .

[30]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[31]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[32]  Menghao Qin,et al.  Simulation of coupled heat and moisture transfer in air-conditioned buildings , 2009 .

[33]  Genichi Taguchi,et al.  Taguchi's Quality Engineering Handbook , 2004 .

[34]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[35]  Chih-Hung Wu,et al.  A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression , 2009, Expert Syst. Appl..

[36]  V. I. Hanby,et al.  UK office buildings archetypal model as methodological approach in development of regression models for predicting building energy consumption from heating and cooling demands , 2013 .

[37]  Liu Yang,et al.  Sensitivity analysis and energy conservation measures implications , 2008 .

[38]  Yi Zhang,et al.  Parallel EnergyPlus and the development of a parametric analysis tool. , 2009 .

[39]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .