Stochastic modelling of flexible load characteristics of split-type air conditioners using grey-box modelling and random forest method

Split-type air conditioners (ACs) are the major contributors to the total electricity use and peak power demand in residential buildings due to their high penetration. Accurate prediction of their energy con-sumption plays a significant role in demand side management (DSM), because it can help exploit the demand response (DR) potential of these ACs fully. However, the existing studies on the load character-istics modelling of split-type ACs are still not comprehensive, especially in the consideration of occupant behavior and appliance characteristics. To solve this problem, this study has developed a novel flexible load characteristics model by combining the stochastic method, grey-box modelling (i.e., equivalent ther-mal parameter (ETP) model) and random forest method. The stochastic method can help capture the dynamic occupants' energy-related behaviors considering various family structures. Random forest can address the difficulties in simulating the power characteristics of variable-speed ACs under various oper-ating conditions. With the prediction error of 2.8%, the proposed model can effectively characterize the energy performance of split-type ACs at both individual and aggregate levels. To better show the load flexibility of ACs, an investigation on DR potential of split-type ACs was also carried out by resetting tem-perature setpoints. The result showed that the maximum DR potentials at the scope of 1000 aggregated households are 394.54 kWh and 323.82 kWh on weekdays and weekends, respectively. The developed flexible load characteristics model of ACs can be used by utility companies for DR potential assessment and model-based DR control for a single household or building clusters.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study develops a novel flexible load characteristics model for split-type air conditioners (ACs) by combining stochastic, grey-box modeling, and random forest methods. The model accurately predicts the energy consumption of split-type ACs and explores the demand response potential. The results show that the model has a prediction error of 2.8% and effectively characterizes the energy performance of ACs.