Daily power demand prediction for buildings at a large scale using a hybrid of physics-based model and generative adversarial network

Power demand prediction for buildings at a large scale is required for power grid operation. The bottom-up prediction method using physics-based models is popular, but has some limitations such as a heavy workload on model creation and long computing time. Top-down methods based on data driven models are fast, but less accurate. Considering the similarity of power demand patterns of single buildings and the superiority of generative adversarial network (GAN), this paper proposes a new method (E-GAN), which combines a physics-based model (EnergyPlus) and a data-driven model (GAN), to predict the daily power demand for buildings at a large scale. The new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power demands. Utilizing the prediction for those typical buildings, the GAN then is adopted to forecast the power demands of a large number of buildings. To verify the proposed method, the E-GAN is used to predict 24-hour power demands for a set of residential buildings. The results show that (1) 4.3% of physics-based models in each building category are required to ensure the prediction accuracy; (2) compared with the physics-based model, the E-GAN can predict power demand accurately with only 5% error (measured by mean absolute percentage error, MAPE) while using only approximately 9% of the computing time; and (3) compared with data-driven models (e.g., support vector regression, extreme learning machine, and polynomial regression model), E-GAN demonstrates at least 60% reduction in prediction error measured by MAPE.

Automated summary

This paper proposes a new method (E-GAN) that combines a physics-based model (EnergyPlus) and a data-driven model (GAN) to predict the daily power demand for buildings at a large scale. The results show that E-GAN can accurately predict power demand with shorter computing time compared to the physics-based model.