Clustering-based forecasting method for individual consumers electricity load using time series representations
This paper presents a new method for forecasting a load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Before clustering, time series are efficiently preproces...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2018-07-01
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| Series: | Open Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/comp-2018-0006 |
| _version_ | 1818728266181115904 |
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| author | Laurinec Peter Lucká Mária |
| author_facet | Laurinec Peter Lucká Mária |
| author_sort | Laurinec Peter |
| collection | DOAJ |
| description | This paper presents a new method for forecasting a load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Before clustering, time series are efficiently preprocessed by normalisation and the computation of various model-based time series representation methods. Final centroid-based forecasts are scaled by saved normalisation parameters to create the forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on three smart meter datasets from residences of Ireland and Australia, and factories of Slovakia. The achieved results proved that our clustering-based method improves forecasting accuracy mainly for residential consumers.We can also proclaim that it can be found such time series representation and clustering setting that will our forecasting method perform more accurately than fully disaggregated approach. Our method is also more scalable since it is necessary to train the model only on clusters and not for every consumer separately |
| first_indexed | 2024-12-17T22:27:15Z |
| format | Article |
| id | doaj.art-394b61ac73ed453ca2d807e70db8788f |
| institution | Directory Open Access Journal |
| issn | 2299-1093 |
| language | English |
| last_indexed | 2024-12-17T22:27:15Z |
| publishDate | 2018-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Computer Science |
| spelling | doaj.art-394b61ac73ed453ca2d807e70db8788f2022-12-21T21:30:19ZengDe GruyterOpen Computer Science2299-10932018-07-0181385010.1515/comp-2018-0006comp-2018-0006Clustering-based forecasting method for individual consumers electricity load using time series representationsLaurinec Peter0Lucká Mária1Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Ilkovičova 2, Bratislava, Slovak RepublicFaculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Ilkovičova 2, Bratislava, Slovak RepublicThis paper presents a new method for forecasting a load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Before clustering, time series are efficiently preprocessed by normalisation and the computation of various model-based time series representation methods. Final centroid-based forecasts are scaled by saved normalisation parameters to create the forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on three smart meter datasets from residences of Ireland and Australia, and factories of Slovakia. The achieved results proved that our clustering-based method improves forecasting accuracy mainly for residential consumers.We can also proclaim that it can be found such time series representation and clustering setting that will our forecasting method perform more accurately than fully disaggregated approach. Our method is also more scalable since it is necessary to train the model only on clusters and not for every consumer separatelyhttps://doi.org/10.1515/comp-2018-0006clusteringtime series data miningelectricity load forecastingsmart grid |
| spellingShingle | Laurinec Peter Lucká Mária Clustering-based forecasting method for individual consumers electricity load using time series representations Open Computer Science clustering time series data mining electricity load forecasting smart grid |
| title | Clustering-based forecasting method for individual consumers electricity load using time series representations |
| title_full | Clustering-based forecasting method for individual consumers electricity load using time series representations |
| title_fullStr | Clustering-based forecasting method for individual consumers electricity load using time series representations |
| title_full_unstemmed | Clustering-based forecasting method for individual consumers electricity load using time series representations |
| title_short | Clustering-based forecasting method for individual consumers electricity load using time series representations |
| title_sort | clustering based forecasting method for individual consumers electricity load using time series representations |
| topic | clustering time series data mining electricity load forecasting smart grid |
| url | https://doi.org/10.1515/comp-2018-0006 |
| work_keys_str_mv | AT laurinecpeter clusteringbasedforecastingmethodforindividualconsumerselectricityloadusingtimeseriesrepresentations AT luckamaria clusteringbasedforecastingmethodforindividualconsumerselectricityloadusingtimeseriesrepresentations |