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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2019

25.08.2017 | Original Article

Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network

verfasst von: Coşkun Hamzaçebi, Hüseyin Avni Es, Recep Çakmak

Erschienen in: Neural Computing and Applications | Ausgabe 7/2019

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Abstract

Electricity is one of the most important end-user energy types in today’s world and has an effective role in development of societies and economies. Stability of electricity supply is provided by matching of generated and consumed electricity amount during the all-day. So, electricity consumption forecasting is an essential issue for electric utilities. In this study, the monthly electricity demand of Turkey has been predicted. To model the effects of seasonality and trend, four different ANN models have been developed and selected the superior one. In addition, the selected ANN model has been compared with SARIMA model in order to increase the acceptability and reliability of the ANN model. The monthly electricity demand of Turkey has been predicted between 2015 and 2018 via the ANN model that can make successful and high-accuracy predictions according to the performance measures. The forecasting values will help in determining the medium-term and stable energy policies.
Vorheriger Artikel Modifying CMAC adaptive control with weight smoothing in order to avoid overlearning and bursting
Nächster Artikel A reproducing kernel Hilbert space pseudospectral method for numerical investigation of a two-dimensional capillary formation model in tumor angiogenesis problem

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Metadaten
Titel
Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network
verfasst von
Coşkun Hamzaçebi
Hüseyin Avni Es
Recep Çakmak
Publikationsdatum
25.08.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3183-5

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