自下而上的预测:使用智能电表数据进行负荷预测的应用和局限性

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Harsh Anand, R. Nateghi, Negin Alemazkoor
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引用次数: 0

摘要

可靠的短期负荷预测对于电力系统的规划和运行至关重要。短期负荷预测是电力采购、发电、调度、负荷切换等环节的重要组成部分,对平衡电力供需、降低电力短缺风险具有重要意义。鉴于能源部门向碳中和技术的过渡,这一点变得更加重要。具体来说,由于可再生能源本身具有不确定性,与传统能源部门相比,具有可再生发电机组的分布式能源系统更依赖于准确的负荷预测来进行需求响应管理。尽管有大量关于预测电力需求的文献,但大多数研究只关注基于前一步总需求的观察来预测总需求。随着智能电表技术的进步和高分辨率用电数据的可用性,利用高分辨率智能电表数据进行负荷预测越来越受到关注。使用智能电表数据的研究主要涉及一种“自下而上”的方法,即在亚总量水平上开发单独的预测模型,并将预测汇总以估计总需求。虽然这种方法有利于结合精细分辨率数据进行负荷预测,但它有几个缺点,可能导致次优预测。然而,这些缺点在负荷预测文献中几乎没有得到承认。这项工作表明,这种自下而上的负荷预测方法所施加的限制可能会导致误导性的结果,从而阻碍碳中和电网内的有效负荷管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bottom-up forecasting: Applications and limitations in load forecasting using smart-meter data
Abstract Reliable short-term load forecasting is vital for the planning and operation of electric power systems. Short-term load forecasting is a critical component used in purchasing and generating electric power, dispatching, and load switching, which is essential for balancing supply and demand and mitigating the risk of power shortages. This is becoming even more critical given the transition to carbon-neutral technologies in the energy sector. Specifically, since renewable sources are inherently uncertain, a distributed energy system with renewable generation units is more heavily dependent on accurate load forecasts for demand-response management than traditional energy sectors. Despite extensive literature on forecasting electricity demand, most studies focus on predicting the total demand solely based on the previous-step observations of aggregate demand. With advances in smart-metering technology and the availability of high-resolution consumption data, harnessing fine-resolution smart-meter data in load forecasting has attracted increasing attention. Studies using smart-meter data mainly involve a “bottom-up” approach that develops separate forecast models at sub-aggregate levels and aggregates the forecasts to estimate the total demand. While this approach is conducive to incorporating fine-resolution data for load forecasting, it has several shortcomings that can result in sub-optimal forecasts. However, these shortcomings are hardly acknowledged in the load forecasting literature. This work demonstrates how limitations imposed by such a bottom-up load forecasting approach can lead to misleading results, which could hamper efficient load management within a carbon-neutral grid.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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