Investigation on the operation strategy of a hybrid BIPV/T facade in plateau areas: An adaptive regulation method based on artificial neural network

Controlling the work mode of building energy equipment by the artificial neural network (ANN) is an advanced technology for improving the indoor thermal environment. However, few investigations were conducted on the application of ANN in controlling BIPV/T systems. In this work, a regulation method based on ANN was developed to control the operation strategy of a hybrid BIPV/T facade. To evaluate the function of the ANN control system, an air-conditioned room installed with the hybrid BIPV/T facade was constructed as the study case. Firstly, the developed ANN model was trained and verified. Then, the comparison between the ANN-based and conventional regulation methods was conducted based on the meteorological conditions of the Qinghai-Tibet Plateau. Finally, the energy-saving performances of the ANN-based hybrid BIPV/T facade in the typical plateau areas were analyzed. The main results are: (1) The developed ANN model had an error of less than 1% in predicting the indoor temperature. (2) The ANN based regulation method could eliminate the overcooling/overheating problem and decrease the airconditioning load by 165.0 kWh in Xining and 255.9 kWh in Lhasa. (3) The ANN-based hybrid BIPV/T facade could reduce the total energy consumption of studied plateau areas by more than 40%. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The research developed a regulation method based on ANN to control the operation of a hybrid BIPV/T facade, which significantly reduced energy consumption by more than 40% in typical plateau areas. The ANN control system had an error of less than 1% in predicting indoor temperature and eliminated overcooling/overheating issues, decreasing the air-conditioning load significantly.