Dynamic Regression Prediction Models for Customer Specific Electricity Consumption
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Electricity |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4826/4/2/12 |
| _version_ | 1797595179749736448 |
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| author | Fatlinda Shaqiri Ralf Korn Hong-Phuc Truong |
| author_facet | Fatlinda Shaqiri Ralf Korn Hong-Phuc Truong |
| author_sort | Fatlinda Shaqiri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T02:32:59Z |
| format | Article |
| id | doaj.art-b7ec6d01d5a34d6099e9bbdd4cc6c4f3 |
| institution | Directory Open Access Journal |
| issn | 2673-4826 |
| language | English |
| last_indexed | 2024-03-11T02:32:59Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electricity |
| spelling | doaj.art-b7ec6d01d5a34d6099e9bbdd4cc6c4f32023-11-18T10:07:11ZengMDPI AGElectricity2673-48262023-06-014218521510.3390/electricity4020012Dynamic Regression Prediction Models for Customer Specific Electricity ConsumptionFatlinda Shaqiri0Ralf Korn1Hong-Phuc Truong2Department of Mathematics, RPTU Kaiserslautern-Landau, 67653 Kaiserslautern, GermanyDepartment of Mathematics, RPTU Kaiserslautern-Landau, 67653 Kaiserslautern, GermanyDepartment of Financial Mathematics, Fraunhofer ITWM, 67653 Kaiserslautern, GermanyWe 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.https://www.mdpi.com/2673-4826/4/2/12short-term load forecastingtime series forecastingdynamic regression models |
| spellingShingle | Fatlinda Shaqiri Ralf Korn Hong-Phuc Truong Dynamic Regression Prediction Models for Customer Specific Electricity Consumption Electricity short-term load forecasting time series forecasting dynamic regression models |
| title | Dynamic Regression Prediction Models for Customer Specific Electricity Consumption |
| title_full | Dynamic Regression Prediction Models for Customer Specific Electricity Consumption |
| title_fullStr | Dynamic Regression Prediction Models for Customer Specific Electricity Consumption |
| title_full_unstemmed | Dynamic Regression Prediction Models for Customer Specific Electricity Consumption |
| title_short | Dynamic Regression Prediction Models for Customer Specific Electricity Consumption |
| title_sort | dynamic regression prediction models for customer specific electricity consumption |
| topic | short-term load forecasting time series forecasting dynamic regression models |
| url | https://www.mdpi.com/2673-4826/4/2/12 |
| work_keys_str_mv | AT fatlindashaqiri dynamicregressionpredictionmodelsforcustomerspecificelectricityconsumption AT ralfkorn dynamicregressionpredictionmodelsforcustomerspecificelectricityconsumption AT hongphuctruong dynamicregressionpredictionmodelsforcustomerspecificelectricityconsumption |