使用 Holt-Winters 和 Prophet 算法的高效负荷预测技术,可减轻 COVID-19 中耗电量的影响

IF 1.6 Q4 ENERGY & FUELS
W. Waheed, Qingshan Xu
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引用次数: 0

摘要

强烈建议在智能电网和建筑的新架构中对未来发电实施有效的长期负荷预测。这种方法对于智能电网的稳定性、电力需求估算和改进能源管理系统至关重要,将加强高效需求响应与分布式可再生能源之间的整合。然而,由于受到气候、社会和季节等因素的影响,要进行高精度的能源预测具有相当大的挑战性。COVID-19 范例具有多样性和复杂性,为了估算 COVID-19 范例之前和期间的负荷需求,作者提出并整合了 Holt-Winters 和 Prophet 算法等时间序列预测技术。与 Holt-Winters 模型相比,先知模型的抗噪能力更强。此外,通过使用美国德克萨斯州休斯顿市每小时驱动的电力消耗数据,对这两种模型进行了通用性测试,结果表明先知模型优于霍尔特-温特斯模型。讨论了由此产生的制约因素和影响因素,并从关键结果中验证了实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient load forecasting technique by using Holt‐Winters and Prophet algorithms to mitigate the impact on power consumption in COVID‐19
It is strongly recommended to implement effective long‐term load forecasting for future power generation in the new architecture of the smart grid and buildings. This method is essential for the smart grid's stability, power demand estimation, and an improved energy management system, which will enhance integration between efficient demand response and distributed renewable energy sources. However, due to influencing elements including climatic, societal, and seasonal aspects, it is quite challenging to perform energy prediction with high accuracy. To estimate the load demand before and during the time period of the COVID‐19 paradigm with its diversity and complexity, the authors present and integrate time series forecasting techniques such as Holt‐Winters and Prophet algorithms. In comparison to the Holt‐Winters model, the Prophet model has shown to be more noise‐resistant. Additionally, the Prophet model surpasses the Holt‐Winters model according to the generalisability test of the two models by using the hourly driven power consumption data from Houston, Texas, USA. The resultant constraints and influential factors are discussed, and experimental results can be validated from the pivotal outcome.
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来源期刊
IET Energy Systems Integration
IET Energy Systems Integration Engineering-Engineering (miscellaneous)
CiteScore
5.90
自引率
8.30%
发文量
29
审稿时长
11 weeks
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