Building energy demand patterns for department stores in Korea

The daily energy demand patterns for 14 department stores located in the five largest cities in Korea are surveyed to establish building-energy load models. Electricity and fuel consumption data are field-measured at one carefully chosen store among the sample stores that best represent the statistical characteristics of the department stores to extract hourly consumption profiles for the electricity, heating, hot water, and cooling loads. The data statistically processed to develop a generic computational model for future applications.

[1]  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 .

[2]  Joseph Virgone,et al.  Development and validation of regression models to predict monthly heating demand for residential buildings , 2008 .

[3]  Mohammad. Rasul,et al.  Energy conservation measures in an institutional building in sub-tropical climate in Australia , 2010 .

[4]  K. W. Mui,et al.  Cooling load calculations in subtropical climate , 2007 .

[5]  David Infield,et al.  Domestic lighting: A high-resolution energy demand model , 2009 .

[6]  K. Wojdyga,et al.  An influence of weather conditions on heat demand in district heating systems , 2008 .

[7]  Leif Gustavsson,et al.  Owners perception on the adoption of building envelope energy efficiency measures in Swedish detached houses , 2010 .

[8]  Graeme Maidment,et al.  Forecasting future cooling demand in London , 2009 .

[9]  Abdullah Yildiz,et al.  Energy and exergy analyses of space heating in buildings , 2009 .

[10]  Marco Filippi,et al.  Energy demand for space heating through a statistical approach : application to residential buildings , 2008 .

[11]  Mustafa Inalli,et al.  Impacts of some building passive design parameters on heating demand for a cold region , 2006 .

[12]  Huang Xing-hua,et al.  Influence of energy demands ratio on the optimal facility scheme and feasibility of BCHP system , 2008 .

[13]  Thomas Olofsson,et al.  Energy load predictions for buildings based on a total demand perspective , 1998 .

[14]  Jon Hand,et al.  CONTRASTING THE CAPABILITIES OF BUILDING ENERGY PERFORMANCE SIMULATION PROGRAMS , 2008 .

[15]  T. Olofsson,et al.  Long-term energy demand predictions based on short-term measured data , 2001 .

[16]  Hanan Taleb,et al.  Developing sustainable residential buildings in Saudi Arabia: A case study , 2011 .

[17]  Keith Baker,et al.  Improving the prediction of UK domestic energy-demand using annual consumption-data , 2008 .

[18]  Martin Skitmore,et al.  Using genetic algorithms and linear regression analysis for private housing demand forecast , 2008 .

[19]  Sau Man Lai,et al.  Integration of trigeneration system and thermal storage under demand uncertainties , 2010 .

[20]  Christian Inard,et al.  Fast method to predict building heating demand based on the design of experiments , 2009 .

[21]  Ramachandran Kannan,et al.  The development and application of a temporal MARKAL energy system model using flexible time slicing , 2011 .

[22]  I. G. Capeluto,et al.  Climatic considerations in school building design in the hot-humid climate for reducing energy consumption , 2009 .

[23]  T. Frank,et al.  Climate change impacts on building heating and cooling energy demand in Switzerland , 2005 .

[24]  Martin Ordenes,et al.  The impact of building-integrated photovoltaics on the energy demand of multi-family dwellings in Brazil , 2007 .

[25]  G. Mihalakakou,et al.  Using principal component and cluster analysis in the heating evaluation of the school building sector , 2010 .

[26]  Mo Chung,et al.  Development of a Energy Demand Estimator for Community Energy Systems , 2009 .

[27]  Koen Steemers,et al.  Behavioural, physical and socio-economic factors in household cooling energy consumption , 2011 .

[28]  Bahram Moshfegh,et al.  Energy demand and indoor climate in a low energy building—changed control strategies and boundary conditions , 2006 .

[29]  Mo Chung,et al.  Building Load Models for Hotels in Korea , 2009 .

[30]  Viktor Dorer,et al.  Thermally activated building systems (TABS): Energy efficiency as a function of control strategy, hydronic circuit topology and (cold) generation system , 2011 .

[31]  Gianni Bidini,et al.  Implementation of a cogenerative district heating : Optimization of a simulation model for the thermal power demand , 2006 .

[32]  J. Widén,et al.  A high-resolution stochastic model of domestic activity patterns and electricity demand , 2010 .

[33]  David Pearlmutter,et al.  Evaluating the impact of canyon geometry and orientation on cooling loads in a high-mass building in a hot dry environment , 2010 .

[34]  Thomas Olofsson,et al.  A method for predicting the annual building heating demand based on limited performance data , 1998 .

[35]  Enrico Fabrizio,et al.  The impact of indoor thermal conditions, system controls and building types on the building energy demand , 2008 .

[36]  K.S.Y. Wan,et al.  Representative building design and internal load patterns for modelling energy use in residential buildings in Hong Kong , 2004 .

[37]  Linda Barelli,et al.  Evaluation of the corrected seasonal energy demand, for buildings classification, to be compared with a standard performance scale , 2009 .

[38]  David Pearlmutter,et al.  The effect of urban evaporation on building energy demand in an arid environment , 2008 .