An overview of load demand and price forecasting methodologies
In this work, an overview of the various methodologies developed in recent years for short, mid and long term load and price forecasting is carried out. In the analysis the advantages and disadvantages of each method are introduced, together with the factors that influencing the different types of f...
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2011
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| Online Access: | http://hdl.handle.net/11073/8167 |
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| _version_ | 1864513435868332032 |
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| author | Kourtis, George |
| author2 | Hadjipaschalis, Ioannis Poullikkas, Andreas |
| author2_role | author author |
| author_facet | Kourtis, George Hadjipaschalis, Ioannis Poullikkas, Andreas |
| author_role | author |
| dc.creator.none.fl_str_mv | Kourtis, George Hadjipaschalis, Ioannis Poullikkas, Andreas |
| dc.date.none.fl_str_mv | 2011 2016-03-01T09:08:28Z 2016-03-01T09:08:28Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Kourtis, George, Ioannis Hadjipaschalis, and Andreas Poullikkas. "An overview of load demand and price forecasting methodologies." International Journal of Energy and Environment 2, no. 1 (2011): 123–150. 2076-2895 2076-2909 http://hdl.handle.net/11073/8167 |
| dc.language.none.fl_str_mv | en_US |
| dc.relation.none.fl_str_mv | http://www.ijee.ieefoundation.org/vol2/issue1/IJEE_09_v2n1.pdf |
| dc.subject.none.fl_str_mv | load forecasting price forecasting unit commitment artificial neural networks |
| dc.title.none.fl_str_mv | An overview of load demand and price forecasting methodologies |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | In this work, an overview of the various methodologies developed in recent years for short, mid and long term load and price forecasting is carried out. In the analysis the advantages and disadvantages of each method are introduced, together with the factors that influencing the different types of forecasting. Unless the effects of these factors are well taken into consideration errors can occur in the forecasting results and that results in increasing operational costs. The analysis indicates that the best suited method for all types of forecasting is artificial neural network, which outperforms better with nonlinear functions and on weekend days or national holidays. If are not to be distinguished from week day data, weekend and national holidays data a good alternative would be an autoregressive integrated moving average based method. |
| format | article |
| id | aus_e720a43e855d1daea670ddbd4ab96b28 |
| identifier_str_mv | Kourtis, George, Ioannis Hadjipaschalis, and Andreas Poullikkas. "An overview of load demand and price forecasting methodologies." International Journal of Energy and Environment 2, no. 1 (2011): 123–150. 2076-2895 2076-2909 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/8167 |
| publishDate | 2011 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | An overview of load demand and price forecasting methodologiesKourtis, GeorgeHadjipaschalis, IoannisPoullikkas, Andreasload forecastingprice forecastingunit commitmentartificial neural networksIn this work, an overview of the various methodologies developed in recent years for short, mid and long term load and price forecasting is carried out. In the analysis the advantages and disadvantages of each method are introduced, together with the factors that influencing the different types of forecasting. Unless the effects of these factors are well taken into consideration errors can occur in the forecasting results and that results in increasing operational costs. The analysis indicates that the best suited method for all types of forecasting is artificial neural network, which outperforms better with nonlinear functions and on weekend days or national holidays. If are not to be distinguished from week day data, weekend and national holidays data a good alternative would be an autoregressive integrated moving average based method.2016-03-01T09:08:28Z2016-03-01T09:08:28Z2011info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfKourtis, George, Ioannis Hadjipaschalis, and Andreas Poullikkas. "An overview of load demand and price forecasting methodologies." International Journal of Energy and Environment 2, no. 1 (2011): 123–150.2076-28952076-2909http://hdl.handle.net/11073/8167en_UShttp://www.ijee.ieefoundation.org/vol2/issue1/IJEE_09_v2n1.pdfoai:repository.aus.edu:11073/81672024-08-22T12:15:51Z |
| spellingShingle | An overview of load demand and price forecasting methodologies Kourtis, George load forecasting price forecasting unit commitment artificial neural networks |
| status_str | publishedVersion |
| title | An overview of load demand and price forecasting methodologies |
| title_full | An overview of load demand and price forecasting methodologies |
| title_fullStr | An overview of load demand and price forecasting methodologies |
| title_full_unstemmed | An overview of load demand and price forecasting methodologies |
| title_short | An overview of load demand and price forecasting methodologies |
| title_sort | An overview of load demand and price forecasting methodologies |
| topic | load forecasting price forecasting unit commitment artificial neural networks |
| url | http://hdl.handle.net/11073/8167 |