Learning Customer Behaviour under Real-Time Pricing in the Smart Grid

In this paper, we propose two learning models to study the electricity consumption patterns of customers responding to real-time prices. We firstly divide home appliances into non-shift able appliances, shift able appliances and curtail able appliances according to their load types. Since non-shift able appliances have fixed operation routines, the consumption patterns of these appliances are obvious, thus need no learning. A learning model based on mean price ranking has been designed with the aim to study the electricity consumption patterns of using shift able appliances. For curtail able appliances, we propose a learning model based on multiple linear regression to learn the consumption pattern of customers. The simulation results in this paper show that the proposed learning models are feasible and efficient. Most importantly, this paper provides a new perspective for further research in learning and analysing the behaviour of electricity customers in the context of the smart grid.