PeakTK: An Open Source Toolkit for Peak Forecasting in Energy Systems

Phuthipong Bovornkeeratiroj, John Wamburu, David E. Irwin, P. Shenoy
{"title":"PeakTK: An Open Source Toolkit for Peak Forecasting in Energy Systems","authors":"Phuthipong Bovornkeeratiroj, John Wamburu, David E. Irwin, P. Shenoy","doi":"10.1145/3530190.3534791","DOIUrl":null,"url":null,"abstract":"As the electric grid undergoes the transition to a carbon free future, many new techniques for optimizing the grid’s energy usage and carbon footprint are being designed. A common technique used by many approaches is to reduce the energy usage of the grid’s peak demand periods since doing so is beneficial for reducing the carbon usage of the grid. Consequently, the design of peak forecasting methods that predict when and how much peak demand will be seen is at the heart of many energy optimization approaches. In this paper, we present PeakTK, an open-source toolkit and reference datasets for peak forecasting in energy systems. PeakTK implements a range of peak forecasting methods that have been proposed recently and exposes them through well-defined interfaces and library modules. Our goal is to improve reproducibility of energy systems research by providing a common framework for evaluating and comparing new peak forecasting algorithms. Further, PeakTK provides libraries to enable researchers and practitioners to easily incorporate peak forecasting methods into their research when implementing higher level grid optimizations. We discuss the design and implementation of PeakTK and present case studies to demonstrate how PeakTK can be used for forecasting or quantitative comparisons of energy optimization methods.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3530190.3534791","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

As the electric grid undergoes the transition to a carbon free future, many new techniques for optimizing the grid’s energy usage and carbon footprint are being designed. A common technique used by many approaches is to reduce the energy usage of the grid’s peak demand periods since doing so is beneficial for reducing the carbon usage of the grid. Consequently, the design of peak forecasting methods that predict when and how much peak demand will be seen is at the heart of many energy optimization approaches. In this paper, we present PeakTK, an open-source toolkit and reference datasets for peak forecasting in energy systems. PeakTK implements a range of peak forecasting methods that have been proposed recently and exposes them through well-defined interfaces and library modules. Our goal is to improve reproducibility of energy systems research by providing a common framework for evaluating and comparing new peak forecasting algorithms. Further, PeakTK provides libraries to enable researchers and practitioners to easily incorporate peak forecasting methods into their research when implementing higher level grid optimizations. We discuss the design and implementation of PeakTK and present case studies to demonstrate how PeakTK can be used for forecasting or quantitative comparisons of energy optimization methods.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
PeakTK:一个用于能源系统峰值预测的开源工具包
随着电网向无碳未来过渡,许多优化电网能源使用和碳足迹的新技术正在被设计出来。许多方法使用的一种常用技术是减少电网高峰需求期的能源使用,因为这样做有利于减少电网的碳使用。因此,峰值预测方法的设计是许多能源优化方法的核心,该方法可以预测何时以及将看到多少峰值需求。在本文中,我们提出了PeakTK,一个用于能源系统峰值预测的开源工具包和参考数据集。PeakTK实现了一系列最近提出的峰值预测方法,并通过定义良好的接口和库模块公开它们。我们的目标是通过提供一个评估和比较新的峰值预测算法的通用框架来提高能源系统研究的可重复性。此外,PeakTK提供了库,使研究人员和实践者能够在实现更高级别的网格优化时轻松地将峰值预测方法合并到他们的研究中。我们讨论了PeakTK的设计和实现,并提出了案例研究,以展示PeakTK如何用于预测或定量比较能源优化方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
NOTE: Unavoidable Service to Unnoticeable Risks: A Study on How Healthcare Record Management Opens the Doors of Unnoticeable Vulnerabilities for Rohingya Refugees Making AI Explainable in the Global South: A Systematic Review Note: A Sociomaterial Perspective on Trace Data Collection: Strategies for Democratizing and Limiting Bias Complexity of Factor Analysis for Particulate Matter (PM) Data: A Measurement Based Case Study in Delhi-NCR Note: Urbanization and Literacy as factors in Politicians’ Social Media Use in a largely Rural State: Evidence from Uttar Pradesh, India
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1