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Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches
Author(s) -
Nasr G. E.,
Badr E. A.,
Younes M. R.
Publication year - 2002
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.766
Subject(s) - univariate , multivariate statistics , mean absolute percentage error , mean squared error , proxy (statistics) , artificial neural network , statistics , econometrics , electric energy consumption , energy consumption , multivariate analysis , standard deviation , gross domestic product , mathematics , computer science , engineering , artificial intelligence , economics , electric energy , power (physics) , physics , quantum mechanics , economic growth , electrical engineering
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.