Uncertainty modeling of household appliance loads for smart energy management

Households represent a large share of flexibility on the demand side for assisting the smooth operation of the highly renewable grid. The stochastic nature of activities and various appliance usage patterns of household occupants affects the flexibility potential greatly, but is usually oversimplified in the existing research. This paper aims to propose a novel method for simulating the dynamics of household energy-related activities and appliance usage, which provides realistic synthetic load profiles for smart energy management. To this aim, a highly resolved multi-agent system model is proposed, which comprises an Agent-Based Activity Chain Model (ABACM) and 34 types of common household appliance models. Firstly, the patterns of various occupant behaviors are obtained through mining of real-world residential time-of-use data. Then, the stochastic activity profiles of occupants are generated using the ABACM, with a root mean square error of 0.95%. Finally, the electricity consumption profiles of household appliances are simulated based on specific energy-related activities. The proposed method is validated and proved to be able to capture stochastic occupant behaviors and represent the dynamics of residential energy consumption. The future work will focus on using the proposed model for exploring the potential of residential demand response.