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Recognizing the states of household appliance is helpful to monitor the power consumption and model user behaviors at home. Non-Intrusive Load Monitoring (NILM) receives widespread attention as it can identify a individual appliance state using a single sensor. However, presented approaches today can not be adopted in actual home scenarios because they either ignore the energy limitation of sensors or require a complex user configuration. To solve this problem, this paper proposes IEHouse which is a Non-Intrusive Household Appliance State Recognition System. It leverages a supervised learning process over the labeled appliance data sets which can be constructed dynamically based on a small number of appliance profiles. It uses Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) models, to identify appliance states and improves the accuracy through …