Neural networks in forecasting electrical energy consumption
This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting in Lebanon. In order to provide the forecasted energy consumption, the ANN interpolates among the EEC and its determinants in a training data set. In this study, four ANN models are prese...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| التنسيق: | article |
| منشور في: |
2002
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/3160 http://dx.doi.org/10.1002/er.766 http://onlinelibrary.wiley.com/doi/10.1002/er.766/full |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513460169080832 |
|---|---|
| author | Nasr, George |
| author2 | Badr, E.A. Younes, M.R. |
| author2_role | author author |
| author_facet | Nasr, George Badr, E.A. Younes, M.R. |
| author_role | author |
| dc.creator.none.fl_str_mv | Nasr, George Badr, E.A. Younes, M.R. |
| dc.date.none.fl_str_mv | 2002 2016-02-23T08:16:19Z 2016-02-23T08:16:19Z 2016-02-23 |
| dc.identifier.none.fl_str_mv | 0363-907X http://hdl.handle.net/10725/3160 http://dx.doi.org/10.1002/er.766 Nasr, G. E., Badr, E. A., & Younes, M. R. (2002). Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches. International Journal of Energy Research, 26(1), 67-78. http://onlinelibrary.wiley.com/doi/10.1002/er.766/full |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | International journal of energy research |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Neural networks in forecasting electrical energy consumption Univariate and multivariate approaches |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting in Lebanon. In order to provide the forecasted energy consumption, the ANN interpolates among the EEC and its determinants in a training data set. In this study, four ANN models are presented and implemented on real EEC data. The first model is a univariate model based on past consumption values. The second model is a multivariate model based on EEC time series and a weather-dependent variable, namely, degree days (DD). The third model is also a multivariate model based on EEC and a gross domestic product (GDP) proxy, namely, total imports (TI). Finally, the fourth model combines EEC, DD and TI. Forecasting performance measures such as mean square errors (MSE), mean absolute deviations (MAD), mean percentage square errors (MPSE) and mean absolute percentage errors (MAPE) are presented for all models. Copyright © 2002 John Wiley & Sons, Ltd. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_ff977045b7bcee3e75611020bafd0cda |
| identifier_str_mv | 0363-907X Nasr, G. E., Badr, E. A., & Younes, M. R. (2002). Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches. International Journal of Energy Research, 26(1), 67-78. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/3160 |
| publishDate | 2002 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Neural networks in forecasting electrical energy consumptionUnivariate and multivariate approachesNasr, GeorgeBadr, E.A.Younes, M.R.This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting in Lebanon. In order to provide the forecasted energy consumption, the ANN interpolates among the EEC and its determinants in a training data set. In this study, four ANN models are presented and implemented on real EEC data. The first model is a univariate model based on past consumption values. The second model is a multivariate model based on EEC time series and a weather-dependent variable, namely, degree days (DD). The third model is also a multivariate model based on EEC and a gross domestic product (GDP) proxy, namely, total imports (TI). Finally, the fourth model combines EEC, DD and TI. Forecasting performance measures such as mean square errors (MSE), mean absolute deviations (MAD), mean percentage square errors (MPSE) and mean absolute percentage errors (MAPE) are presented for all models. Copyright © 2002 John Wiley & Sons, Ltd.PublishedN/A2016-02-23T08:16:19Z2016-02-23T08:16:19Z20022016-02-23Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article0363-907Xhttp://hdl.handle.net/10725/3160http://dx.doi.org/10.1002/er.766Nasr, G. E., Badr, E. A., & Younes, M. R. (2002). Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches. International Journal of Energy Research, 26(1), 67-78.http://onlinelibrary.wiley.com/doi/10.1002/er.766/fullenInternational journal of energy researchinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/31602019-01-14T11:33:21Z |
| spellingShingle | Neural networks in forecasting electrical energy consumption Nasr, George |
| status_str | publishedVersion |
| title | Neural networks in forecasting electrical energy consumption |
| title_full | Neural networks in forecasting electrical energy consumption |
| title_fullStr | Neural networks in forecasting electrical energy consumption |
| title_full_unstemmed | Neural networks in forecasting electrical energy consumption |
| title_short | Neural networks in forecasting electrical energy consumption |
| title_sort | Neural networks in forecasting electrical energy consumption |
| url | http://hdl.handle.net/10725/3160 http://dx.doi.org/10.1002/er.766 http://onlinelibrary.wiley.com/doi/10.1002/er.766/full |