基于整体的增强短期和中期负载预测,使用优化的缺失值归算
Tania Gupta1, Richa Bhatia2, Sachin Sharma3
1Department of Electronics and Communication, Netaji Subhas University of Technology, East-Campus (formerly AIACTR, affiliated to GGSIPU, Dwarka), New Delhi, India.
Scientific reports
|July 2, 2025
概括
准确的电力负载预测对公用事业公司至关重要. 本研究引入了一个集体投票回归模型,并使用归算方法来改进能源消耗预测,增强规划和管理.
科学领域:
- 能源系统 能源系统
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 有效的电力负载预测对于公用事业公司的运营规划,能源管理和市场参与至关重要.
- 准确的电力使用预测对于满足客户需求和优化能源分配至关重要.
- 现有的预测方法可能受到数据质量问题的限制,例如缺失的值.
研究的目的:
- 开发和验证一种使用集体投票回归方法的新型电力负载预测模型.
- 引入一个归算技术来处理能源消耗数据中缺失的值,以提高预测准确度.
- 用实时数据将拟议模型的性能与使用最先进的方法进行比较.
主要方法:
- 为预测电力负载,实施了集体投票回归模型.
- 开发并验证了一种新的归算方法,用于解决能源消耗数据集中缺少的数据.
- 在实时数据集上,以10-30%的速度使用模拟缺失数据测试了归算方法的有效性.
- 使用诸如平均绝对百分比误差 (MAPE),平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 等指标来评估性能.
主要成果:
- 拟议的归算方法有效地处理了不同缺失率的能源消耗数据中的缺失值.
- 与其他方法相比,集体投票回归预测模型在预测准确度方面取得了显著的改进.
- 该模型在预测前一天和前一周消耗的电力负载方面取得了卓越的性能.
结论:
- 开发的归算方法提高了用于预测的能源消耗数据的可靠性.
- 集体投票回归模型为电力负载预测提供了强大而准确的解决方案.
- 这种方法为公用事业公司提供了改进的能源管理和规划工具.
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