Renewable electric energy estimation by hyperparameter optimization using hybrid deep learning method

Kübra Kaysal; Ahmet Haşim Yurttakal; Fatih Onur Hocaoğlu

Makale detayları

Renewable electric energy estimation by hyperparameter optimization using hybrid deep learning method

Yazarlar:

Kübra Kaysal | Ahmet Haşim Yurttakal | Fatih Onur Hocaoğlu

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Mühendisliği

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 3, pp.770-777

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001295897

pISSN:

2564-6605

Anahtar kelimeler:

Özet:

 The unregulated and unconscious use of energy sources causes environmental damage. On the other hand the increasing population density, development of industry and technology increase the demand for electrical energy day by day. For this purpose, investments in the energy sector are directed towards renewable energy sources such as wind energy in order to ensure both environment-friendly and supply-demand balance. The amount of energy obtained from wind energy varies depending on regional differences such as wind direction and speed. In this study, a method is proposed to achieve better results in predicting electricity generation from wind energy by capturing the non-linear and non-stationary nature of wind energy using a hybrid approach of deep learning methods, specifically CNN and BLSTM architectures. In the forecasting model, 26280 real-time data measured at hourly frequency are used. In addition, the hyperparameter values used in the model were optimized using the Grid Search algorithm in order to increase the prediction success. The success of the proposed hybrid model is compared with the BLSTM model. As a result, the R2 value, which indicates the success rate of the proposed CNN-BLSTM model, was calculated as 0.984.

 The unregulated and unconscious use of energy sources causes environmental damage. On the other hand the increasing population density, development of industry and technology increase the demand for electrical energy day by day. For this purpose, investments in the energy sector are directed towards renewable energy sources such as wind energy in order to ensure both environment-friendly and supply-demand balance. The amount of energy obtained from wind energy varies depending on regional differences such as wind direction and speed. In this study, a method is proposed to achieve better results in predicting electricity generation from wind energy by capturing the non-linear and non-stationary nature of wind energy using a hybrid approach of deep learning methods, specifically CNN and BLSTM architectures. In the forecasting model, 26280 real-time data measured at hourly frequency are used. In addition, the hyperparameter values used in the model were optimized using the Grid Search algorithm in order to increase the prediction success. The success of the proposed hybrid model is compared with the BLSTM model. As a result, the R2 value, which indicates the success rate of the proposed CNN-BLSTM model, was calculated as 0.984.

 The unregulated and unconscious use of energy sources causes environmental damage. On the other hand the increasing population density, development of industry and technology increase the demand for electrical energy day by day. For this purpose, investments in the energy sector are directed towards renewable energy sources such as wind energy in order to ensure both environment-friendly and supply-demand balance. The amount of energy obtained from wind energy varies depending on regional differences such as wind direction and speed. In this study, a method is proposed to achieve better results in predicting electricity generation from wind energy by capturing the non-linear and non-stationary nature of wind energy using a hybrid approach of deep learning methods, specifically CNN and BLSTM architectures. In the forecasting model, 26280 real-time data measured at hourly frequency are used. In addition, the hyperparameter values used in the model were optimized using the Grid Search algorithm in order to increase the prediction success. The success of the proposed hybrid model is compared with the BLSTM model. As a result, the R2 value, which indicates the success rate of the proposed CNN-BLSTM model, was calculated as 0.984.

 Enerji kaynaklarının düzensiz ve bilinçsiz kullanımı çevresel tahribatlara sebep olurken, artan nüfus yoğunluğu, sanayi ve teknolojinin gelişmesi de her geçen gün elektrik enerjisi talebini artırmaktadır. Bu amaçla enerji sektörü, yatırımlarını hem çevre dostu hem de arz-talep dengesini sağlamak adına rüzgâr enerjisi gibi yenilenebilir enerji kaynaklarına yöneltmektedir. Rüzgâr enerjisinden elde edilen enerji miktarı rüzgârın yönü ve hızı gibi bölgesel farklılıklara bağlı olarak değişkenlik göstermektedir. Bu çalışmada, rüzgâr enerjisinden elektrik üretimi tahmininde daha iyi sonuçlar elde etmek amacıyla rüzgâr enerjisinin doğrusal ve durağan olmayan yapısını yakalamak için derin öğrenme metotlarından CNN ve BLSTM mimarilerinin hibrit bir şekilde kullanıldığı bir yöntem önerilmektedir. Tahmin modelinde, saatlik frekansta ölçülmüş 26280 adet gerçek zamanlı veri kullanılmıştır. Ayrıca tahmin başarısını artırmak adına modelde kullanılan hiperparametre değerleri Grid Search arama algoritması kullanılarak optimize edilmiştir. Önerilen hibrit modelin başarısı BLSTM modeli ile kıyaslanmıştır. Sonuç olarak önerilen CNN-BLSTM modelinin başarım oranını gösteren R2 değeri 0.984 olarak hesaplanmıştır.

Makale detayları

Renewable electric energy estimation by hyperparameter optimization using hybrid deep learning method

Yazarlar:

Kübra Kaysal | Ahmet Haşim Yurttakal | Fatih Onur Hocaoğlu

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Mühendisliği

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 3, pp.770-777

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001295897

pISSN:

2564-6605

Renewable electric energy

Deep learning

Hyperparameter optimizationYenilenebilir elektrik enerjisi

Derin öğrenme

Hiperparametre optimizasyonu

Sayı

Makale detayları

Renewable electric energy estimation by hyperparameter optimization using hybrid deep learning method

Yazarlar: Kübra Kaysal | Ahmet Haşim Yurttakal | Fatih Onur Hocaoğlu

Yayıncı: Nigde Omer Halisdemir University

Ana Başlıklar: Elektrik Mühendisliği

Dergi: Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 3, pp.770-777

Belge Tipi: Makale

Yayın Tarihi: 7/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001295897

pISSN: 2564-6605

Renewable electric energy

Deep learning

Hyperparameter optimizationYenilenebilir elektrik enerjisi

Derin öğrenme

Hiperparametre optimizasyonu

Sayı

Yazarlar:

Kübra Kaysal | Ahmet Haşim Yurttakal | Fatih Onur Hocaoğlu

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Mühendisliği

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.12 Issue 3, pp.770-777

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001295897

pISSN:

2564-6605