将高维二次回归与基于惩罚的水电厂预测建模集成为准确的关税预测
Ritesh Dash1, Anupa Sinha2, Abinash Mahapatro3
1Department of Computer Science Engineering, Kalinga University, Raipur, India.
Scientific reports
|July 12, 2025
概括
本研究引入了一种新的二次回归模型,用于准确地预测微型电网中的发电成本和销售价格. 与现有方法相比,该模型大大减少了预测错误,优化了金融操作.
科学领域:
- 能源系统工程 能源系统工程
- 计算经济学计算经济学
- 数据科学数据科学数据科学
背景情况:
- 在微型电网中优化水电站运营需要准确预测每单位发电成本和能源市场的销售价格.
- 像SVR,SARIMA和LSTM这样的传统预测方法在捕捉复杂的市场动态方面存在局限性.
研究的目的:
- 开发和验证一种新的高维二次回归模型,对预测水电发电成本和销售价格进行惩罚.
- 提高现有预测模型的准确性和概括能力.
主要方法:
- 开发了一个高维的二次回归模型,包含多项式相互作用项和L2规范化.
- 12个特征,包括操作变量和非线性组合,使用异常值检测,规范化和插值进行了预处理.
- 该模型与SVR,SARIMA和LSTM进行了基准测试,使用多个时间间隔的关键性能指标.
主要成果:
- 与基准模型相比,拟议的模型始终实现了最低的预测误差.
- 该模型在水电-DISCOM集成中显示出高准确度和适用于即时市场预测的适用性.
- 用100个参数和2秒的训练时间实现了计算效率.
结论:
- 新的二次回归模型为预测水电微型电网中的能源成本和价格提供了一种优越的方法.
- 提供了实时部署和集成到混合智能预测框架的建议.
- 未来的工作可以扩展模型,以纳入政策,天气动态和影响分析,以提高价格预测.
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