Data-driven room classification for office buildings based on echo state network

In this paper, based on echo state network (ESN), a data-driven method is developed to solve the room classification problem of office buildings. The developed method is divided into two steps. Given the data of electricity consumption, which are classified into electricity consumption from sockets, lights and air-conditioners for a typical room in an office building, the first step is to reconstruct the behavior of electricity consumption in three types by using three ESNs. The second step is to classify the room into a certain category of office room, computer room, storage room and meeting room by establishing another ESN. The developed method fully utilizes the outstanding performance of ESN in chaotic time-series prediction and classification. Practical study on an office building illustrates the accuracy and effectiveness of the developed method.