现在使用增强组合机器播放下一个小时的住宅负载
Ali Muqtadir1, Bin Li2, Zhou Ying3
1School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, 102206, China. alimuqtadir@ncepu.edu.cn.
Scientific reports
|February 28, 2025
概括
通过整合LightGBM,XGBoost和CatBoost模型,提高了准确的住宅负载预测. 这种新的整体方法通过克服现有方法的局限性来提高能源管理和电网稳定性.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的住宅负载预测对于电网稳定性和能源管理至关重要.
- 非线性,季节性能源使用和随机用户行为对传统预测模型构成挑战.
- 现有的合奏方法在可扩展性和通用性方面扎,导致过度拟合和复杂性.
研究的目的:
- 提出一个综合组合模型,将LightGBM,XGBoost和CatBoost结合起来,以改进住宅负载预测.
- 利用每个模型的独特优势,提高准确性和概括性.
- 在可扩展性和多变量数据管理方面解决现有的双向组合方法的局限性.
主要方法:
- 集成LightGBM用于多站点概括,XGBoost用于防止过拟合,以及CatBoost用于分类特征管理.
- 使用来自北美和欧洲13个住宅地点的真实世界数据集进行实施.
- 使用包括根平均平方对数误差 (RMSLE),R平方 (R2),根平均平方误差 (RMSE),RMSE变化系数 (CVRMSE) 和平均偏差误差 (MBE) 在内的指标进行性能评估.
主要成果:
- 拟议的综合模型实现了0.1898的最低RMSLE和0.9745.2的R2.
- 在多个评估指标中表现优于最先进的算法.
- 一项废除研究证实了增量模型集成的预测效果.
结论:
- 集成的LightGBM,XGBoost和CatBoost模型为住宅负载预测提供了强大而准确的解决方案.
- 这种方法有效地解决了动态用户行为和复杂数据的挑战,增强了能源管理策略.
- 与现有方法相比,该模型展示了优越的性能和概括能力.
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