{"title":"Dynamic Regression Prediction Models for Customer Specific Electricity Consumption","authors":"Fatlinda Shaqiri, R. Korn, Hong-Phuc Truong","doi":"10.3390/electricity4020012","DOIUrl":null,"url":null,"abstract":"We have developed a conventional benchmark model for the prediction of two days of electricity consumption for industrial and institutional customers of an electricity provider. This task of predicting 96 values of 15 min of electricity consumption per day in one shot is successfully dealt with by a dynamic regression model that uses the Seasonal and Trend decomposition method (STL) for the estimation of the trend and the seasonal components based on (approximately) three years of real data. With the help of suitable R packages, our concept can also be applied to comparable problems in electricity consumption prediction.","PeriodicalId":35642,"journal":{"name":"Electricity Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electricity Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/electricity4020012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 1
Abstract
We have developed a conventional benchmark model for the prediction of two days of electricity consumption for industrial and institutional customers of an electricity provider. This task of predicting 96 values of 15 min of electricity consumption per day in one shot is successfully dealt with by a dynamic regression model that uses the Seasonal and Trend decomposition method (STL) for the estimation of the trend and the seasonal components based on (approximately) three years of real data. With the help of suitable R packages, our concept can also be applied to comparable problems in electricity consumption prediction.
Electricity JournalBusiness, Management and Accounting-Business and International Management
CiteScore
5.80
自引率
0.00%
发文量
95
审稿时长
31 days
期刊介绍：
The Electricity Journal is the leading journal in electric power policy. The journal deals primarily with fuel diversity and the energy mix needed for optimal energy market performance, and therefore covers the full spectrum of energy, from coal, nuclear, natural gas and oil, to renewable energy sources including hydro, solar, geothermal and wind power. Recently, the journal has been publishing in emerging areas including energy storage, microgrid strategies, dynamic pricing, cyber security, climate change, cap and trade, distributed generation, net metering, transmission and generation market dynamics. The Electricity Journal aims to bring together the most thoughtful and influential thinkers globally from across industry, practitioners, government, policymakers and academia. The Editorial Advisory Board is comprised of electric industry thought leaders who have served as regulators, consultants, litigators, and market advocates. Their collective experience helps ensure that the most relevant and thought-provoking issues are presented to our readers, and helps navigate the emerging shape and design of the electricity/energy industry.
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