A Markov-chain Activity-based Model for Pedestrians in Office Buildings

As the number of people working in office buildings increases, there is an urgent need to improve building services, such as lighting and temperature control, within these buildings to increase energy efficiency and well-being of occupants. A pedestrian behaviour model that simulates office occupants’ movements and locations can provide the high spatial and temporal resolution data required for the testing, evaluation, and optimization of these control systems. However, since most studies in pedestrian research focus on modelling specific actions at the operational level or target situations where movement schedules do not have to modelled, a pedestrian behaviour model that can simulate complex situations over long time periods is missing. Therefore, this paper proposes a tactical level model to generate occupant movement patterns in office buildings. The Markov-chain activity-based model proposed here is data parsimonious, flexible in accepting different levels of information, and can produce high resolution output. The mathematical properties of the methodology are analyzed to understand their impact on the final results. Finally, the tactical level pedestrian behaviour model is face validated using a case study of an imaginary office with a simple layout

Thermal comfort, occupant control behaviour and performance gap – a study of office buildings in north-east China using data mining

Simulation techniques have been increasingly applied to building performance evaluation and building environmental design. However, uncertain and random factors, such as occupant behaviour, can generate a performance gap between the results from computer simulations and real buildings. This study involved a longitudinal questionnaire survey conducted for one year, along with a continuous recording of environmental parameters and behaviour state changes, in ten offices located in the severe cold region of north-east China. The offices varied from private rooms to open-plan spaces. The thermal comfort experiences of the office workers and their environmental control behaviours were tracked and analysed during summer and winter seasons. The interaction of the thermal comfort experiences of the occupants and behaviour changes were analysed, and window-opening behaviour patterns were defined by applying data mining techniques. The results also generated window-opening behaviour working profiles to link to building performance simulation software. The aim was to apply these profiles to further study the discrepancies between simulation and monitored results that arise from real-world occupant behaviour patterns

Adaptive thermal comfort and its application in mixed mode buildings: The case of a hot-summer and cold-winter climate in China

It is widely recognised that one’s ability of adaptation is remarkable and thermal comfort is significantly related to such adaptations. This study proposes an alternative method of predicting adaptive thermal comfort based on the availability of adaptations, in particular behavioural adaptations, which needs quantifications of individual adaptation processes and of interactions between them. The fundamental argument of this method is that exercising an adaptive behaviour leads to an increase in comfort temperature, which is termed adaptive increment in this study. Apart from adaptive increments, this method also determines a baseline thermal comfort temperature (the thermal comfort temperature without adaptations) and a correction factor that considers the factors affecting adaptive behaviours, based on which, the highest operative temperature at which people may still feel thermally comfortable. This may be applied in mixed mode (MM) buildings to achieve a higher air-conditioning (AC) setpoint which may lead to a significant reduction in cooling energy. This method is believed to be flexible in dealing with different environments with various levels of adaptations and likely to be advantageous over the steady-state and adaptive models in predicting thermal comfort temperature of an environment with abundant adaptive opportunities. This study also evaluates ways of promoting the use of adaptive opportunities. It explores how adaptive thermal comfort theories may be used for behaviour modelling and in turn be applied to enhance the energy performances and comfort levels of real buildings. To improve the feasibility of this method key effective adaptive behaviours are studied in detail through lab experiments and field studies. The lab experiment has found the adaptive increment of taking cold water to be 1.5°C which is more significant than the previous literature suggests. When all the studied adaptive behaviours are exercised, the overall adaptive increment is as high as 4.7°C. However, the research has identified some issues associated with the adaptive opportunities studied. These include the existence of constraints on the use of adaptive behaviours, the low availability of some effective adaptive opportunities, the low operation frequency of desk fans and the misuse of windows and AC systems. Despite this, the availability of more adaptive opportunities has been verified to be capable of increasing the highest operative temperature at which people may still feel thermally comfortable: the lab experiment shows that over 80% of the participants can still find it thermally comfortable at an operative temperature of 30°C on the condition that adequate adaptive opportunities are provided; the field study shows that the thermal comfort temperature of occupants increases by at least 1°C when desk fans and cool mats are available. Based on these analyses, it proposes an MM system which encourages occupants to exercise adaptive opportunities and improves both comfort levels and energy efficiency. Building performance simulation results show that the proposed MM system is effective in reducing the reliance on AC systems and promotes effective uses of windows and AC systems. By applying the MM system and the associated passive energy-saving strategies, an office can cut cooling energy by about 90% and the peak cooling load by over 80% during transitional seasons