Version 1
: Received: 6 November 2023 / Approved: 7 November 2023 / Online: 7 November 2023 (09:00:39 CET)
How to cite:
Huang, Y.; Huang, Q.; Wu, W.; Ye, P.; Li, H.; Yan, Q. Optimization of Roof Photovoltaic Design for Industrial Plants Based on MIV-BP Neural Network. Preprints2023, 2023110424. https://doi.org/10.20944/preprints202311.0424.v1
Huang, Y.; Huang, Q.; Wu, W.; Ye, P.; Li, H.; Yan, Q. Optimization of Roof Photovoltaic Design for Industrial Plants Based on MIV-BP Neural Network. Preprints 2023, 2023110424. https://doi.org/10.20944/preprints202311.0424.v1
Huang, Y.; Huang, Q.; Wu, W.; Ye, P.; Li, H.; Yan, Q. Optimization of Roof Photovoltaic Design for Industrial Plants Based on MIV-BP Neural Network. Preprints2023, 2023110424. https://doi.org/10.20944/preprints202311.0424.v1
APA Style
Huang, Y., Huang, Q., Wu, W., Ye, P., Li, H., & Yan, Q. (2023). Optimization of Roof Photovoltaic Design for Industrial Plants Based on MIV-BP Neural Network. Preprints. https://doi.org/10.20944/preprints202311.0424.v1
Chicago/Turabian Style
Huang, Y., Hao Li and Qixiang Yan. 2023 "Optimization of Roof Photovoltaic Design for Industrial Plants Based on MIV-BP Neural Network" Preprints. https://doi.org/10.20944/preprints202311.0424.v1
Abstract
The study aims to analyze the characteristic parameters of rooftop photovoltaic (PV) power generation on industrial plant buildings in the Ningxia region of China, in order to evaluate the impact of passive design characteristic parameters on the benefits of PV power generation and determine the degree of impact of different passive design characteristic parameters. The methodology involves analyzing the characteristics of existing industrial plant buildings in Ningxia, China, and conducting a series of regional studies on the parameters of plant PV power generation influencing factors. The simulation results revealed that five features, including roof form, PV panel laying pattern, PV panel laying area, azimuth angle, and PV module material, have a significant impact on PV power generation benefits of industrial plant buildings.The study further uses MATLAB 2022b to build a backpropagation neural network model with 5 neurons in the implicit layer to predict the PV power generation benefits. The model calculates the whole-life cycle power generation benefits and the whole-life cycle reduction of carbon emissions as output data. Finally, the Mean Impact Value (MIV) method is employed to select the feature parameter values one by one within their range, and the passive design feature parameters that have the greatest influence on the prediction results are identified as the PV panel laying method, the PV panel tilt angle, and the PV material parameters, respectively.
Keywords
Photovoltaic Power Generation; Industrial Building; MIV-BP Neural Network; Design Optimization
Subject
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
