Data-Driven Aggregate Thermal Dynamic Model for Buildings: A Regression Approach

The thermal inertia of buildings brings considerable flexibility to the building heating and cooling loads, which is believed to be a promising demand response resource in energy systems. However, it is challenging to utilize the thermal inertia of buildings in the operation of energy systems because of the complicated thermal dynamics and high computational cost. This paper proposes a data-driven aggregate thermal dynamic model (ATDM) for the multi-zone building and building cluster, respectively, which offers an equivalent and low-complexity building model for the operation and control of energy systems. The ATDM consists of the aggregation equation and the state equation. The former projects the detailed real states of buildings into the characteristic state (i.e., aggregate state) using an affine function, and the latter describes the thermal dynamics of buildings using the aggregate state. The ATDM is formulated for two practical load control strategies for the building cluster, including direct load control and indirect load control. Then, the constrained nonlinear regression model is proposed to estimate the model parameters and occupant behavior, for which an efficient algorithm based on the block coordinate descent method is developed by exploiting the decomposable structure of the regression model. Simulation results based on real-world data show that the root mean square error and mean absolute percentage error for the multi-zone building (or building cluster) are below 0.72 degrees C and 1.44% (or 0.32 degrees C and 1.39%), respectively, verifying the effectiveness of the proposed methods.

Automated summary

This paper proposes a data-driven aggregate thermal dynamic model for the operation and control of building clusters in energy systems. It offers an equivalent and low-complexity building model by aggregating the detailed real states of buildings and utilizing their thermal inertia. Simulation results based on real-world data demonstrate the effectiveness of the proposed methods.