An occupant-based energy consumption prediction model for office equipment

Abstract Occupant energy demand plays an important role in whole building energy consumption. To improve building energy efficiency, the stochastic characteristics of occupant behavior should be explored. In this paper, an occupant-based energy consumption prediction model was proposed based on the analysis of the relationship between occupant behavior and equipment energy consumption, drawing from an indoor occupancy rate model and computer input power model. Polynomial and Markov chain–Monte Carlo methods were applied to describe the time-varying indoor occupancy rate and the computer input power in multi-occupant office rooms. The computer energy consumption and occupant activity were related through the time-varying indoor occupancy rate. The energy consumption of office equipment was calculated by time accumulation and necessary correction. Three office buildings with different functions were selected as case studies, which are mainly used for business, administration and scientific research. The error rate between the predicted energy consumption from the model and actual energy consumption record was below 5%.

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

[2]  Bauke de Vries,et al.  Methods for the prediction of intermediate activities by office occupants , 2010 .

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

[4]  Guido Carpinelli,et al.  Multi-linear Monte Carlo simulation method for probabilistic load flow of distribution systems with wind and photovoltaic generation systems , 2015 .

[5]  Fernanda Leite,et al.  Integrating probabilistic methods for describing occupant presence with building energy simulation models , 2014 .

[6]  O. T. Masoso,et al.  The dark side of occupants’ behaviour on building energy use , 2010 .

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

[8]  Andrea Masini,et al.  The impact of behavioural factors in the renewable energy investment decision making process: Conceptual framework and empirical findings , 2012 .

[9]  Qiang Fu,et al.  Influence of indoor design air parameters on energy consumption of heating and air conditioning , 2013 .

[10]  Kelvin K. W. Yau,et al.  Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .

[11]  David E. Gunderson,et al.  Understanding high performance buildings: The link between occupant knowledge of passive design systems, corresponding behaviors, occupant comfort and environmental satisfaction , 2015 .

[12]  Darren Robinson,et al.  A bottom-up stochastic model to predict building occupants' time-dependent activities , 2013 .

[13]  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 .

[14]  J. Widén,et al.  Forecasting household consumer electricity load profiles with a combined physical and behavioral approach , 2014 .

[15]  J. Torriti A review of time use models of residential electricity demand , 2014 .

[16]  Tianzhen Hong,et al.  An insight into actual energy use and its drivers in high-performance buildings , 2014 .

[17]  Henrik Madsen,et al.  Dynamic modeling of presence of occupants using inhomogeneous Markov chains , 2014 .

[18]  Darren Robinson,et al.  A generalised stochastic model for the simulation of occupant presence , 2008 .

[19]  Jie Zhao,et al.  Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining , 2014 .

[20]  Aref Y. Maalej,et al.  Dynamic modelling and simulation of a new air conditioning prototype by solar energy , 2007 .