Feature Engineering of Weather Data for Short-Term Energy Consumption Forecast

被引：3

作者：

Sinimaa, Maria ^{[1
]}

Spichakova, Margarita ^{[1
]}

Belikov, Juni ^{[1
]}

Petlenkov, Eduard ^{[2
]}

机构：

[1] Tallinn Univ Technol, Dept Software Sci, Tallinn, Estonia

[2] Tallinn Univ Technol, Dept Comp Syst, Tallinn, Estonia

来源：

2021 IEEE MADRID POWERTECH | 2021年

关键词：

Energy consumption; modeling; short-term load forecast; day-ahead market; linear regression;

D O I：

10.1109/PowerTech46648.2021.9494920

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Development of accurate models for short-term load forecast forms a cornerstone problem on the modern energy market. Recent studies propose various methods mainly grouped to statistical and machine learning techniques. The current paper continues these studies and introduces an extended version of our previously proposed model by including several important weather parameters obtained via detailed analysis of possible combinations and data transformations. This model is validated using real data and indicates the total improvement of the original model by 11.49%.

引用

页数：6

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