ESG guidance and artificial intelligence support for power systems analytics in the energy industry

• Published in: Scientific reports Vol. 14; no. 1; p. 11347
• Main Authors: Li, Qingjiang, Zou, Guilin, Zeng, Wenlong, Gao, Jie, He, Feipeng, Zhang, Yujun
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 18.05.2024; Nature Publishing Group UK; Nature Portfolio
• Subjects: Accuracy; Algorithms; Artificial intelligence; Energy; Energy industry; Fault diagnosis; Forecast accuracy; Forecasting; Forecasting models; Governance; Long short-term memory; Neural networks; Predictions; Systems analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-61491-8

In order to increase the precision and effectiveness of power system analysis and fault diagnosis, this study aims to assess the power systems in the energy sector while utilizing artificial intelligence (AI) and environmental social governance (ESG). First, the ESG framework is presented in this study to fully account for the effects of the power system on the environment, society, and governance. Second, to coordinate the operation of various components and guarantee the balance and security of the power system, the CNN-BiLSTM power load demand forecasting model is built by merging convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Lastly, the particle swarm optimization (PSO) algorithm is used to introduce and optimize the deep belief network (DBN), and a power grid fault diagnostic model is implemented using the PSO technique and DBN. The model's performance is assessed through experimentation. The outcomes demonstrate how the CNN-BiLSTM algorithm significantly increases forecasting accuracy while overcoming the drawback of just having one dimension of power load data. The values of 0.054, 0.076, and 0.102, respectively, are the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Effective processing of large-scale nonlinear data is achieved in the area of power grid fault diagnosis, resulting in prediction accuracy of 96.22% and prediction time of only 129.94 s. This is clearly better than other algorithms and increases fault prediction efficiency and accuracy. Consequently, the model presented in this study not only produces impressive results in fault diagnosis and load demand forecasting, but also advances the field of power system analysis in the energy industry and offers a significant amount of support for the sustainable and intelligent growth of the energy industry.