Enhancing e-governance in the ministry of electricity in Iraq using artificial intelligence
Irak elektrik bakanlığında e-yönetişimin yapay zeka kullanılarak iyileştirilmesi
- Tez No: 788139
- Danışmanlar: PROF. DR. NİZAMETTİN AYDIN
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: ARIMA, e-governance, energy consumption, long-term electricity demand forecasting, LSTM, Bakanlıklar, Elektronik yönetişim, Enerji, Enerji ve Tabi Kaynaklar Bakanlığı, Irak, Irak-Musul, Yapay zeka, ARIMA, e-governance, energy consumption, long-term electricity demand forecasting, LSTM, Ministry, Electronic governance, Energy, Ministry of Energy and Natural Sources, Iraq, Iraq-Mosul, Artificial intelligence
- Yıl: 2023
- Dil: İngilizce
- Üniversite: Yıldız Teknik Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Bağımlılık Ana Bilim Dalı
- Bilim Dalı: Bilgisayar Bilimi ve Mühendisliği Bilim Dalı
- Sayfa Sayısı: Belirtilmemiş.
Özet
Accurately estimating energy consumption over long periods is critical for companies that distribute and supply electricity, whether they work for the government or the private sector. It is essential to increase the quality of future energy production, especially in countries like Iraq that have been experiencing energy crises for a long time. It is possible to both improve gaps in energy resource needs and accurately estimate energy consumption by implementing artificial intelligence within an electronic governance system. Such a system can also help ensure that specific areas receive the appropriate energy supply by monitoring existing stations and identifying which stations need to be expanded. This determination is possible with accurate reports sent to the relevant authorities at regular intervals. In this study, the two-year electricity consumption data of the Iraqi Ministry of Electricity for the city of Mosul between the years 2020-2021 was used, taking into account the effect of external factors, such as the effect of weather conditions. Various models have been proposed, such as the Exogenous (SARIMax) model, which includes the use of neural networks, and statistical models, such as Long-Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM, Auto-Regression Integrated Moving Average (ARIMA) and Seasonal Auto, and Regressive Integrated Moving Average. To predict future electricity consumption and improve the quality of power generation, the models were trained with two categories of data: with or without weather data. The models were trained over a series of epochs (10, 20, and 30) with three different optimizers for each stack of these epochs. While the Bi-LSTM model had the best results in the experiments, with results of 9.207 RMSE, the statistical models ARIMA and SARIMAx reached RMSE values of 14.610 and 15.743, respectively.
Özet (Çeviri)
Accurately estimating energy consumption over long periods is critical for companies that distribute and supply electricity, whether they work for the government or the private sector. It is essential to increase the quality of future energy production, especially in countries like Iraq that have been experiencing energy crises for a long time. It is possible to both improve gaps in energy resource needs and accurately estimate energy consumption by implementing artificial intelligence within an electronic governance system. Such a system can also help ensure that specific areas receive the appropriate energy supply by monitoring existing stations and identifying which stations need to be expanded. This determination is possible with accurate reports sent to the relevant authorities at regular intervals. In this study, the two-year electricity consumption data of the Iraqi Ministry of Electricity for the city of Mosul between the years 2020-2021 was used, taking into account the effect of external factors, such as the effect of weather conditions. Various models have been proposed, such as the Exogenous (SARIMax) model, which includes the use of neural networks, and statistical models, such as Long-Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM, Auto-Regression Integrated Moving Average (ARIMA) and Seasonal Auto, and Regressive Integrated Moving Average. To predict future electricity consumption and improve the quality of power generation, the models were trained with two categories of data: with or without weather data. The models were trained over a series of epochs (10, 20, and 30) with three different optimizers for each stack of these epochs. While the Bi-LSTM model had the best results in the experiments, with results of 9.207 RMSE, the statistical models ARIMA and SARIMAx reached RMSE values of 14.610 and 15.743, respectively.
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