Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms
Abstract Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand o...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2018-06-01
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| Series: | Vietnam Journal of Computer Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s40595-018-0119-7 |
| _version_ | 1818935703818469376 |
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| author | Dima Alberg Mark Last |
| author_facet | Dima Alberg Mark Last |
| author_sort | Dima Alberg |
| collection | DOAJ |
| description | Abstract Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand of electricity. This paper presents two non-seasonal and two seasonal sliding window-based ARIMA (auto regressive integrated moving average) algorithms. These algorithms are developed for short-term forecasting of hourly electricity load at the district meter level. The algorithms integrate non-seasonal and seasonal ARIMA models with the OLIN (online information network) methodology. To evaluate our approach, we use a real hourly consumption data stream recorded by six smart meters during a 16-month period. |
| first_indexed | 2024-12-20T05:24:23Z |
| format | Article |
| id | doaj.art-df8dbaca5ecb474a9ca635cfcabc5e7d |
| institution | Directory Open Access Journal |
| issn | 2196-8888 2196-8896 |
| language | English |
| last_indexed | 2024-12-20T05:24:23Z |
| publishDate | 2018-06-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Vietnam Journal of Computer Science |
| spelling | doaj.art-df8dbaca5ecb474a9ca635cfcabc5e7d2022-12-21T19:51:56ZengWorld Scientific PublishingVietnam Journal of Computer Science2196-88882196-88962018-06-0153-424124910.1007/s40595-018-0119-7Short-term load forecasting in smart meters with sliding window-based ARIMA algorithmsDima Alberg0Mark Last1Department of Industrial Engineering and Management, SCE-Shamoon College of EngineeringDepartment of Software and Information Systems Engineering, Ben-Gurion University of the NegevAbstract Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand of electricity. This paper presents two non-seasonal and two seasonal sliding window-based ARIMA (auto regressive integrated moving average) algorithms. These algorithms are developed for short-term forecasting of hourly electricity load at the district meter level. The algorithms integrate non-seasonal and seasonal ARIMA models with the OLIN (online information network) methodology. To evaluate our approach, we use a real hourly consumption data stream recorded by six smart meters during a 16-month period.http://link.springer.com/article/10.1007/s40595-018-0119-7Internet of thingsSmart citySmart gridShort-term forecastingIncremental learningOnline information network |
| spellingShingle | Dima Alberg Mark Last Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms Vietnam Journal of Computer Science Internet of things Smart city Smart grid Short-term forecasting Incremental learning Online information network |
| title | Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms |
| title_full | Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms |
| title_fullStr | Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms |
| title_full_unstemmed | Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms |
| title_short | Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms |
| title_sort | short term load forecasting in smart meters with sliding window based arima algorithms |
| topic | Internet of things Smart city Smart grid Short-term forecasting Incremental learning Online information network |
| url | http://link.springer.com/article/10.1007/s40595-018-0119-7 |
| work_keys_str_mv | AT dimaalberg shorttermloadforecastinginsmartmeterswithslidingwindowbasedarimaalgorithms AT marklast shorttermloadforecastinginsmartmeterswithslidingwindowbasedarimaalgorithms |