Estimation of Hourly Utility Usage Using Machine Learning

被引：0

作者：

Wong, Albert ^{[1
]}

Chiu, Chunyin ^{[1
]}

Abdulgapul, Abigail ^{[1
]}

Beg, Mirza Nomaan ^{[1
]}

Khmelevsky, Youry ^{[2
]}

Mahony, Joe ^{[3
]}

机构：

[1] Langara Coll, Math & Stat, Vancouver, BC, Canada

[2] Okanagan Coll, Comp Sci, Kelowna, BC, Canada

[3] Harris SmartWorks, Ottawa, ON, Canada

来源：

SYSCON 2022: THE 16TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON) | 2022年

基金：

加拿大自然科学与工程研究理事会;

关键词：

utility usage; time series; machine learning; deep learning applications; big data;

D O I：

10.1109/SysCon53536.2022.9773816

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The COVID-19 pandemic has had a major impact on the usage of various utilities. To assess the impact, this research explores the (baseline) estimation of hourly utility usage if the pandemic did not happen. Using usage data from Harris SmartWorks, various machine learning algorithms are implemented to show that they are effective in modelling hourly usage patterns, calendar effects, as well as "lingering" effects of the exogenous factors and produce accurate results.

引用

页数：5

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