在能源行业,ESG指导和人工智能支持电力系统分析
Qingjiang Li1, Guilin Zou2, Wenlong Zeng3
1China Southern Power Grid Co., Ltd., Guangzhou, 510000, People's Republic of China.
Scientific reports
|May 18, 2024
概括
这项研究整合了人工智能 (AI) 和环境社会治理 (ESG) 以进行先进的电力系统分析. 它引入了新的负载需求预测和故障诊断模型,提高了能源部门的效率和准确性.
科学领域:
- 电力系统工程 电力系统工程
- 人工智能的人工智能
- 环境社会治理 (ESG)
背景情况:
- 电力系统分析和故障诊断需要提高精度和效率.
- 整合环境社会治理 (ESG) 对于评估电力系统影响至关重要.
- 现有的方法可能缺乏负载需求预测和故障预测的准确性.
研究的目的:
- 评估使用人工智能和ESG来改进分析和故障诊断的电力系统.
- 为准确的电力负载需求预测开发CNN-BiLSTM模型.
- 实现和优化一个深度信念网络 (DBN) 与粒子群优化 (PSO) 用于电网故障诊断.
主要方法:
- 介绍一个ESG框架来评估环境,社会和治理影响.
- 开发一个卷积神经网络-双向长期短期记忆 (CNN-BiLSTM) 模型,用于负载需求预测.
- 优化深信网络 (DBN) 使用粒子群优化 (PSO) 进行故障诊断.
主要成果:
- 在CNN-BiLSTM模型显著提高了预测准确度,低RMSE (0.054),MAE (0.076),和MAPE (0.102).
- 在129.94秒内,PSO优化的DBN在电网故障诊断中实现了96.22%的准确性.
- 拟议的模型在预测和故障预测效率方面都超过了现有的算法.
结论:
- 综合人工智能和ESG方法增强了电力系统分析和故障诊断.
- 该研究为能源行业的可持续和智能增长提供了强有力的框架.
- 开发的模型为电力系统操作的准确性和效率提供了显著的改进.
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