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Full Description
Do you have data on occupant behaviour, indoor environment or energy use in buildings? Are you interested in statistical analysis and modelling? Do you have a specific (research) question and dataset and would like to know how to answer the question with the data available?
Statistical Modelling of Occupant Behaviour covers a range of statistical methods and models used for modelling energy- and comfort-related occupant behaviour in buildings. It is a classical textbook on statistics, including many practical examples related to occupant behaviour that are either taken from real research problems or adapted from such.
The main focus is traditional statistical techniques based on the likelihood principle that can be applied to occupant behaviour modelling, including:
General, generalised linear and survival models
Mixed effect and hierarchical models
Linear time series and Markov models
Linear state space and hidden Markov models
Illustration of all methods using occupant behaviour examples implemented in R
The built environment affects occupants who live and work in it, and occupants affect the built environment by adapting it to their needs - for example, by adapting their indoor environments by interacting with building components and systems. These adaptive behaviours account for great uncertainty in the prediction of building energy use and indoor environmental conditions. Occupant behaviour is complex and multi-disciplinary but can be successfully modelled using statistical approaches.
Statistical Modelling of Occupant Behaviour is written for researchers and advanced practitioners who work with real-world applications and modelling of occupant data. It describes the kinds of statistical models that may be used in various occupant behaviour modelling research. It gives a theoretical overview of these methods and then applies them to the study of occupant behaviour using readily replaceable examples in the R environment that are based on actual and experimental data.
Contents
1. Introduction. 2. Likelihood. 3. General linear models. 4. Generalized linear models. 5. Linear mixed effects. Models. 6. Hierarchical models. 7. Survival Analysis. 8. Linear Time Series Models. 9. Markov chain models. 10. State Space Models. 11. Hidden Markov Models.
