Residential user activities play a crucial role in the energy management of buildings. However, it is often difficult to obtain detailed and sufficient size of activity data for user behaviour study, user thermal comfort, estimation of energy consumption, and more. In this project, we create a Markov chain based stochastic model for residential user activity simulation. This activity data generator has to be trained using Time Use Survey (TUS) data set (in this case Danish TUS was used), then users can generate activity sequences with any of the size.
- The model was developed using the database management system, PostgreSQL, the in-database machine learning library, MADlib, and the programming language Python. Therefore, users first have to install these technologies in their computers. The installation guidelines can be found at their respective website. For Python, we recommend to install Anaconda.
- The required Python package can be found at the requirements.txt
- The Danish TUS data set can be requested at CSSR
(to be continued ...)