An ANN-based model for learning individual customer behavior in response to electricity prices

In this paper, we consider the problem of learning the electricity consumption patterns of an individual residential electricity customer, in response to electricity price signals in a demand response program. Two new methods are presented for predicting the hourly loads using the outdoor temperatures, electricity prices and previous loads. The proposed models are based respectively on a fully connected neural network and a Long–Short-term memory network. Both models deal with the uncertainty of household devices and its indoor temperature. Numerical results show the high performance of the proposed methods in terms of accuracy of the predictions. Both models can learn the consumption patterns and are able to give a good approximation of the load profile given a set of prices and temperatures. The proposed architecture can be used to investigate the price elasticity of demand, which can be used in several applications such as optimal pricing, demand flexibility or carbon emission reduction.