Investigating the Effects of Incorporating Seasonal Variation in a Domestic Active Occupancy Model

Abstract

In order to create an electricity demand model for Demand Side Management (DSM) simulations it is important to accurately model the parameters which affect the quantity of electricity consumption. Domestic electricity use is highly dependent on the activities of the residents and their occupancy patterns. In this work a model for stochastically determining active occupancy has been presented. Active occupancy is defined as the presence of residence at home when they are awake. It is one of the main parameters which influence the extent of electricity use in households. After applying different classification filters on the UK Time Use survey data set, the probabilities of active occupancy transition are determined. Such probabilities values are used to stochastically determine the active occupancy level at every simulation time instance. A novel seasonal classification of TU data has been presented which makes the model compatible of generating active occupancy for different season.