A fuzzy-based customer response prediction model for a day-ahead dynamic pricing system

Demand side management is an important aspect of the newly established smart grid concept. Consequently, real-time pricing plays a crucial role in controlling the power demand in the grid, as it encourages customers to shift their time-insensitive loads to off-peak periods. However, most solutions do not address the realistic behavior of customers. In this paper, customers' behavior is modeled based on two main factors; their awareness to the concept of real-time pricing, and their flexibility in shifting their consumption patterns. A Fuzzy logic Customer Response Prediction Model is presented to predict the customers' demand based on the aforementioned factors and the level of motivation offered by the utility company in terms of a change in price. Unlike most of the research in the field which is concerned with predicting the demand, the core of this model lies in predicting how different customers will behave in a real-time pricing environment based on their levels of flexibility, awareness, and the added motivation, which in turn will shape the overall future demand. Using the presented model, it is expected that customers with higher awareness and flexibility would respond better to dynamic price changes.