新型机器学习方法用于增强智能电网电力使用和使用先进的鱼预测价格的灵活支持向量机器
Yuwei Duan1, Zihan Xu2, Huiyi Chen2
1State Grid Shanghai Electric Power Company Marketing Service Center, Shanghai, 200030, China. Dyw1426@163.com.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种先进的机器学习模型,用于预测智能电网电价和电力使用情况. 这种新的方法显著提高了预测准确性,帮助公用事业公司在电网管理和客户定价方面.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 智能电网需要先进的能源管理,以实现可持续性和效率.
- 随着城市化和技术进步的增加,需要创新的电力使用和价格控制系统.
- 机器学习 (ML) 在智能电网运营中提供了准确预测的潜力.
研究的目的:
- 开发一种ML模型,用于预测智能电网电力使用量和电价.
- 提高智能电网能源预测的准确性和可靠性.
- 通过精确的预测,优化能源管理策略.
主要方法:
- 使用先进的鱼气味调节灵活支向量机 (ASS-FSVM) 进行预测.
- 使用来自气象站,智能电表和市场数据库的数据,然后进行清理和正常化.
- 应用主要组件分析 (PCA) 用于维度减小和特征提取.
- 优化和验证的FSVM模型用于预测功耗和价格.
主要成果:
- 在预测电力消耗方面取得了高准确度 (准确度:98.05%,回忆:98.93%,精度:97.10%,F1得分:98.04%).
- 在电力价格预测方面表现强 (MAPE: 4.32%,RMSE: 5.80%,MSE: 8.50%,MAE: 2.95%).
- 该ASS方法有效地确定了关键的数据集属性,以改善预测.
结论:
- 拟议的ASS-FSVM战略显著提高了智能电网运营的预测准确性.
- 改进的预测使公用事业公司能够加强电网稳定性和需求响应性.
- 准确的价格预测促进了更好的客户定价策略和能源管理.
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