A simplified model of dynamic interior cooling load evaluation for office buildings

Abstract Predicted cooling load is a valuable tool for assessing the operation of air-conditioning systems. Compared with exterior cooling load, interior cooling load is more unpredictable. According to principle components analysis, occupancy was proved to be a typical factor influencing interior cooling loads in buildings. By exploring the regularity of interior disturbances in an office building, a simplified evaluation model for interior cooling load was established in this paper. The stochastic occupancy rate was represented by a Markov transition model. Equipment power, lighting power and fresh air were all related to occupancy rate based on time sequence. The superposition of different types of interior cooling loads was also considered in the evaluation model. The error between the evaluation results and measurement results was found to be lower than 10%. In reference to the cooling loads calculated by the traditional design method and area-based method in case study office rooms, the evaluated cooling loads were suitable for operation regulation.

[1]  Tianzhen Hong,et al.  A data-mining approach to discover patterns of window opening and closing behavior in offices , 2014 .

[2]  Jian Yao,et al.  Determining the energy performance of manually controlled solar shades: A stochastic model based co-simulation analysis , 2014 .

[3]  Dirk Saelens,et al.  Implementing realistic occupant behavior in building energy simulations – the effect on the results of an optimization of office buildings , 2010 .

[4]  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 .

[5]  Rui Neves-Silva,et al.  Stochastic models for building energy prediction based on occupant behavior assessment , 2012 .

[6]  J. Doob.  Stochastic processes , 1953 .

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

[8]  Ricardo Enríquez,et al.  Dynamic integrated method based on regression and averages, applied to estimate the thermal parameters of a room in an occupied office building in Madrid , 2014 .

[9]  Jinkyun Cho,et al.  Development of an energy evaluation methodology to make multiple predictions of the HVAC&R system energy demand for office buildings , 2014 .

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

[11]  Zhaoxia Wang,et al.  Influence of occupancy-oriented interior cooling load on building cooling load design , 2016 .

[12]  Huang-Chia Shih,et al.  A robust occupancy detection and tracking algorithm for the automatic monitoring and commissioning of a building , 2014 .

[13]  Eric Wai Ming Lee,et al.  A study of the importance of occupancy to building cooling load in prediction by intelligent approach , 2011 .

[14]  Jin Woo Moon,et al.  Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings , 2016 .

[15]  Zhaoxia Wang,et al.  Analysis of energy efficiency retrofit schemes for heating, ventilating and air-conditioning systems in existing office buildings based on the modified bin method , 2014 .

[16]  Francis Rubinstein,et al.  Modeling occupancy in single person offices , 2005 .

[17]  Milind Tambe,et al.  Coordinating occupant behavior for building energy and comfort management using multi-agent systems , 2012 .

[18]  Gongsheng Huang,et al.  Re-evaluation of building cooling load prediction models for use in humid subtropical area , 2013 .

[19]  José Luis Míguez,et al.  The use of grey-based methods in multi-criteria decision analysis for the evaluation of sustainable energy systems: A review , 2015 .

[20]  Baizhan Li,et al.  Occupants’ behavioural adaptation in workplaces with non-central heating and cooling systems , 2012 .

[21]  Wu Chen,et al.  A novel multivariate linear prediction model for the marine rotary desiccant air-conditioning by adding a dynamic correction factor , 2015 .

[22]  Abdullatif Ben-Nakhi,et al.  Cooling load prediction for buildings using general regression neural networks , 2004 .

[23]  Elie Azar,et al.  A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings , 2012 .

[24]  Xin Xu,et al.  The optimal period of record for air-conditioning outdoor design conditions , 2014 .

[25]  Huisheng Zhang,et al.  A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation , 2016 .

[26]  Stéphane Ploix,et al.  Simulating the dynamics of occupant behaviour for power management in residential buildings , 2013 .

[27]  A. Regattieri,et al.  Artificial neural network optimisation for monthly average daily global solar radiation prediction , 2016 .

[28]  Zhaoxia Wang,et al.  An occupant-based energy consumption prediction model for office equipment , 2015 .

[29]  Zhu Neng,et al.  An improved office building cooling load prediction model based on multivariable linear regression , 2015 .