使用数据库组合,组合和混合建模技术进行家庭电力消耗预测
Gaikwad Sachin Ramnath1, R Harikrishnan2, S M Muyeen3
1Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed) University, Pune, India.
Scientific reports
|October 2, 2024
概括
改善家庭电力消耗 (HEC) 预测至关重要. 一种新的混合模型显著提高了准确性,使研究人员和公用事业公司受益.
科学领域:
- 能源科学 能源科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 由于时间变化和影响因素,家庭电力消耗 (HEC) 预测是复杂的.
- 准确的HEC预测对于能源管理,电网稳定性和政策制定至关重要.
- 现有的模型在实现高预测准确性方面面临挑战.
研究的目的:
- 开发和验证一种新的方法来提高HEC预测的准确性.
- 探索结合不同数据源和应用先进的机器学习技术的影响.
- 为了解和管理家庭能源使用提供更可靠的工具.
主要方法:
- 利用了两个主要数据集:来自225名印度消费者的问卷调查 (QS) 和月度消费 (MC).
- 创建组合数据集 (QS+MC,QsEq+下个月,QsEq+MC) 进行全面分析.
- 应用了相关性方法,特征工程,数据质量评估,异质集团预测 (HEP) 和混合模型.
主要成果:
- 随机森林在MC数据集上的RMSE为36.18千瓦时,MAE为25.73千瓦时,R2为0.76.
- 在QsEq+MC数据集上的自适应提升显示RMSE为36.77千瓦时,MAE为26.18千瓦时,R2为0.76.
- 拟议的混合型号取得了卓越的结果:RMSE为22.02千瓦时,MAE为13.04千瓦时,R2为0.92.
结论:
- 混合模型在HEC预测中明显优于单个机器学习算法和HEP模型.
- 结合不同的数据集和采用先进的技术,如HEP和混合建模是有效提高准确性.
- 这些发现为研究人员,政策制定者和公用事业公司提供了有价值的见解,以改善能源管理.
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