在COVID19大流行期间建模电力消耗:数据集,模型,结果和研究议程
Zulfiqar Ahmad Khan1, Tanveer Hussain2, Amin Ullah3
1Sejong University, Seoul 143-747, Republic of Korea.
概括
这项研究开发了一种混合模型,以改善COVID-19大流行期间的电力负载预测 (ELF) 准确性. 这种新模型有效地预测了大流行中断的电力消费模式.
科学领域:
- 能源系统 能源系统
- 人工智能的人工智能
- 计量经济学 计量经济学
背景情况:
- 由于COVID-19大流行严重破坏了历史数据模式,影响了传统电力负载预测 (ELF) 算法的准确性.
- 现有的数据集和模型在流行病引起的中断期间预测电力消耗 (EC) 的概括能力有限.
- 一个新的数据集,包括96个住宅客户,涵盖了疫情前后的时期,突出了当前预测方法的挑战.
研究的目的:
- 分析COVID-19大流行对电力消耗 (EC) 和电力负载预测 (ELF) 模型的影响.
- 开发和验证一款混合预测模型,在疫情期间和之后为EC模式提高预测准确度.
- 通过使用专门的数据集,评估拟议模型与现有方法的概括性能.
主要方法:
- 开发了一种混合模型,包括用于特征提取的卷积层,用于时间学习的封闭反复网络,以及用于特征选择的自我注意模块.
- 该模型在96个住宅客户的数据集上进行了训练和评估,包括COVID-19大流行前后的数据.
- 进行了废弃性研究,以证明不同模型组件的性能改善和贡献.
主要成果:
- 拟议的混合模型在专用数据集上显著超过现有的ELF算法.
- 实现了0.56% (大流行前) 和3.46% (大流行后) 的平均平方误差 (MSE) 的平均降低.
- 已证明,根平均平方误差 (RMSE) 的平均减少为1.5% (大流行前) 和5.07% (大流行后),平均绝对百分比误差 (MAPE) 为11.81% (大流行前) 和13.19% (大流行后).
结论:
- 开发的混合模型为电力负载预测提供了更好的概括性和准确性,特别是在流行病等破坏性事件期间.
- 这些发现强调了需要适应性预测模型,能够处理由重大社会事件引起的非静止数据模式.
- 建议进行进一步的研究,以解决在这种前所未有的时期数据的固有变异性,并提高模型的稳定性.
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