Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches
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...
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| Veröffentlicht in: | International journal of energy research 2002-01, Vol.26 (1), p.67-78 |
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| creator | Nasr, G. E. Badr, E. A. Younes, M. R. |
| 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. |
| doi_str_mv | 10.1002/er.766 |
| format | Article |
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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.</description><identifier>ISSN: 0363-907X</identifier><identifier>EISSN: 1099-114X</identifier><identifier>DOI: 10.1002/er.766</identifier><identifier>CODEN: IJERDN</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Applied sciences ; Economic data ; Electric energy ; electrical energy consumption ; Energy ; Energy economics ; Exact sciences and technology ; forecasting ; General, economic and professional studies ; Methodology. 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E.</creatorcontrib><creatorcontrib>Badr, E. A.</creatorcontrib><creatorcontrib>Younes, M. R.</creatorcontrib><title>Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches</title><title>International journal of energy research</title><addtitle>Int. J. Energy Res</addtitle><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.</description><subject>Applied sciences</subject><subject>Economic data</subject><subject>Electric energy</subject><subject>electrical energy consumption</subject><subject>Energy</subject><subject>Energy economics</subject><subject>Exact sciences and technology</subject><subject>forecasting</subject><subject>General, economic and professional studies</subject><subject>Methodology. 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R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches</atitle><jtitle>International journal of energy research</jtitle><addtitle>Int. J. Energy Res</addtitle><date>2002-01</date><risdate>2002</risdate><volume>26</volume><issue>1</issue><spage>67</spage><epage>78</epage><pages>67-78</pages><issn>0363-907X</issn><eissn>1099-114X</eissn><coden>IJERDN</coden><abstract>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.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/er.766</doi><tpages>12</tpages></addata></record> |
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| language | eng |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Applied sciences Economic data Electric energy electrical energy consumption Energy Energy economics Exact sciences and technology forecasting General, economic and professional studies Methodology. Modelling neural networks univariate-multivariate modelling |
| title | Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches |
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