Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and ARIMA Model: 2023 Generation Targets by Renewable Energy Resources
Purpose: Türkiye attaches particular importance to the energy production with renewable energy sources in order to overcome the negative economic, environmental and social effects which are caused by fossil resources in energy production. The aim of this study is to propose a model for forecasting t...
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| Format: | Article |
| Language: | English |
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Sanayi ve Teknoloji Bakanlığı
2023-01-01
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| Series: | Verimlilik Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/2110995 |
| _version_ | 1797730931069419520 |
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| author | Özlem Karadağ Albayrak |
| author_facet | Özlem Karadağ Albayrak |
| author_sort | Özlem Karadağ Albayrak |
| collection | DOAJ |
| description | Purpose: Türkiye attaches particular importance to the energy production with renewable energy sources in order to overcome the negative economic, environmental and social effects which are caused by fossil resources in energy production. The aim of this study is to propose a model for forecasting the amount of energy to be produced for Türkiye using renewable energy resources.Methdology: In this study, a forecasting model was created by using the generatio amount of energy generation from renewable sources data between 1965 and 2019 and by using Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) methods.Findings: While it was estimated that 127.516 TWh of energy will be produced in 2023 with the ANN method, this amount was estimated as 45,457 TeraWatt Hours (TWh) with the ARIMA (1,1,6) model. Mean Absolute Percent Error (MAPE) was calculated in order to determine the margin of error of the forecasting models. These values were determined as 13.1% for the ANN model and 21.9% for the ARIMA model. These results show that the ANN model gives a more appropriate estimation result.Originality: In this research, a new model was proposed for the amount of energy to be obtained from RES in Türkiye. It is thought that the results obtained in this study will be useful in energy planning and management. |
| first_indexed | 2024-03-12T11:51:15Z |
| format | Article |
| id | doaj.art-195ee5b38a214c1b8f2b5eb1bb9cf883 |
| institution | Directory Open Access Journal |
| issn | 1013-1388 2757-6973 |
| language | English |
| last_indexed | 2024-03-12T11:51:15Z |
| publishDate | 2023-01-01 |
| publisher | Sanayi ve Teknoloji Bakanlığı |
| record_format | Article |
| series | Verimlilik Dergisi |
| spelling | doaj.art-195ee5b38a214c1b8f2b5eb1bb9cf8832023-08-31T06:43:48ZengSanayi ve Teknoloji BakanlığıVerimlilik Dergisi1013-13882757-69732023-01-0157112113810.51551/verimlilik.1031367417Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and ARIMA Model: 2023 Generation Targets by Renewable Energy ResourcesÖzlem Karadağ Albayrak0KAFKAS UNIVERSITYPurpose: Türkiye attaches particular importance to the energy production with renewable energy sources in order to overcome the negative economic, environmental and social effects which are caused by fossil resources in energy production. The aim of this study is to propose a model for forecasting the amount of energy to be produced for Türkiye using renewable energy resources.Methdology: In this study, a forecasting model was created by using the generatio amount of energy generation from renewable sources data between 1965 and 2019 and by using Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) methods.Findings: While it was estimated that 127.516 TWh of energy will be produced in 2023 with the ANN method, this amount was estimated as 45,457 TeraWatt Hours (TWh) with the ARIMA (1,1,6) model. Mean Absolute Percent Error (MAPE) was calculated in order to determine the margin of error of the forecasting models. These values were determined as 13.1% for the ANN model and 21.9% for the ARIMA model. These results show that the ANN model gives a more appropriate estimation result.Originality: In this research, a new model was proposed for the amount of energy to be obtained from RES in Türkiye. It is thought that the results obtained in this study will be useful in energy planning and management.https://dergipark.org.tr/tr/download/article-file/2110995renewable energy sourcesrenewable energy generation forecastartificial neural networksarima modeltime series forecastenerjiyenilenebilir enerji kaynaklarıyenilenebilir enerji üretimi tahminiyapay sinir ağlarıarima modelizaman serisi tahmini |
| spellingShingle | Özlem Karadağ Albayrak Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and ARIMA Model: 2023 Generation Targets by Renewable Energy Resources Verimlilik Dergisi renewable energy sources renewable energy generation forecast artificial neural networks arima model time series forecast enerji yenilenebilir enerji kaynakları yenilenebilir enerji üretimi tahmini yapay sinir ağları arima modeli zaman serisi tahmini |
| title | Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and ARIMA Model: 2023 Generation Targets by Renewable Energy Resources |
| title_full | Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and ARIMA Model: 2023 Generation Targets by Renewable Energy Resources |
| title_fullStr | Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and ARIMA Model: 2023 Generation Targets by Renewable Energy Resources |
| title_full_unstemmed | Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and ARIMA Model: 2023 Generation Targets by Renewable Energy Resources |
| title_short | Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and ARIMA Model: 2023 Generation Targets by Renewable Energy Resources |
| title_sort | forecasting of renewable energy generation for turkey by artificial neural networks and arima model 2023 generation targets by renewable energy resources |
| topic | renewable energy sources renewable energy generation forecast artificial neural networks arima model time series forecast enerji yenilenebilir enerji kaynakları yenilenebilir enerji üretimi tahmini yapay sinir ağları arima modeli zaman serisi tahmini |
| url | https://dergipark.org.tr/tr/download/article-file/2110995 |
| work_keys_str_mv | AT ozlemkaradagalbayrak forecastingofrenewableenergygenerationforturkeybyartificialneuralnetworksandarimamodel2023generationtargetsbyrenewableenergyresources |