Rüzgar Türbini Enerji Tahmini için RNN Tabanlı Zaman Serisi Analizi

Year 2024, Volume: 6 Issue: 1, 15 - 28, 02.02.2024

Selahattin Barış Çelebi Şehmus Fidan

https://doi.org/10.47933/ijeir.1387314

Abstract

Önemli yenilenebilir enerjinin kaynaklarından biri, sürdürülebilir enerji üretme potansiyeline sahip olan rüzgar enerjisidir. Ancak rüzgâr türbinleri, yüksek ilk yatırım maliyetleri, rüzgâr hızının dinamik yapısı ve rüzgâr açısından verimli enerji bölgeleri bulma problemleri gibi birçok zorluğa sahiptir. Rüzgâr enerjisinin tahmin edilmesi, rüzgâr enerjisi üretiminin etkili bir şekilde planlanması, enerji üretiminin optimizasyonu, şebeke entegrasyonu ve arz güvenliği için çok önemlidir. Bu nedenle yüksek doğrulukta tahminler rüzgar enerjisi sektörünün verimli ve sürdürülebilir bir şekilde çalışmasını sağlar ve enerji güvenliğine, ekonomik istikrara ve çevresel sürdürülebilirliğe katkıda bulunur. Bu çalışma iklimsel verileri kullanarak uzun vadeli rüzgar enerjisi tahmini için tekrarlayan sinir ağlarına (RNN'ler) dayalı bir derin öğrenme (DL) yaklaşımı önermektedir. Bu çalışmanın temelini oluşturan girdi verileri, gerçek dünya koşullarında çalışan bir rüzgâr türbini sisteminden doğrudan elde edilmiştir. Çalışmada önerilen model, karmaşık veri setlerini ve zamana bağlı serileri etkili bir şekilde işlemek için özel olarak tasarlanmış çok katmanlı bir geri yayılım sinir ağı (RNN) mimarisine dayanmaktadır. Modelin mimarisi, karmaşık özellikleri yakalama kapasitesini artırmak için dikkatlice yapılandırılmış, her biri 50 gizli nörona sahip dört ayrı katmandan oluşan bir RNN üzerine inşa edilmiştir. Modelin sağlamlığını artırmak ve aşırı öğrenmeyi önlemek için her RNN katmanını, nöronların %20'sini rastgele devre dışı bırakan bir bırakma (düzenleyici) katmanı takip eder.

Keywords

Tekrarlayan Sinir Ağları, Makine öğrenmesi, Rüzgar gücü tahmini, Regresyon

RNN-Based Time Series Analysis for Wind Turbine Energy Forecasting

Abstract

One significant source of renewable energy is wind power, which has the potential to generate sustainable energy. However, wind turbines have many challenges, such as high initial investment costs, the dynamic nature of wind speed, and the challenge of locating wind-efficient energy regions. Wind power predicting is crucial for effective planning of wind power generation, optimization of power generation, grid integration, and security of supply. Therefore, highly accurate forecasts ensure the efficient and sustainable operation of the wind energy sector and contribute to energy security, economic stability, and environmental sustainability. This study proposes a deep learning (DL) approach based on recurrent neural networks (RNNs) for long-term wind power forecasting utilizing climatic data. The input data that forms the basis of this study is obtained directly from a wind turbine system operating under real-world conditions. The proposed model in this study is based on a multilayer back-propagation neural network (RNN) architecture specifically designed to effectively handle complex data sets and time-dependent series. The architecture of the model is built on an RNN consisting of four separate layers, each with 50 hidden neurons, carefully structured to increase its capacity to capture complex features. To improve the robustness of the model and avoid overlearning, each RNN layer is followed by a dropout (regularizing) layer that randomly deactivates 20% of the neurons to enhance the generalization ability of the network. To finalize the prediction capability of the model, a linear function was chosen in the last layer to directly match the actual values. Evaluating the model performance metrics, the proposed architecture achieved a prediction accuracy of 91% R2 on the test dataset. The findings indicate that proposed method based on multilayer RNN can successfully capture the relationships between the sequential data of the wind turbine.

Keywords

Recurrent Neural Networks, Machine learning, Wind power forecasting, Regression.

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