Makine Öğrenimi Yaklaşımlarını Kullanarak Günlük Deniz Suyu Sıcaklığı Tahmini

Year 2022, , 307 - 318, 30.06.2022

Arif Özbek

https://doi.org/10.21605/cukurovaumfd.1146047

Abstract

Deniz kenarına kurulu nükleer veya kömürle çalışan güç santrallerinde türbin verimliliği doğrudan deniz suyu sıcaklığına (SWT) bağlıdır. Uzun vadeli ortalama iklim koşulları göz önüne alındığında, soğutma ortamı sıcaklığı herhangi bir enerji santralinin tasarımında önemli bir rol oynar. Bu nedenle elektrik üretimindeki verimlilik SWT'deki sapmadan etkilenmektedir. Bu bakımdan, doğru SWT tahmini, santral uygulamalarından elektrik çıkışı için önemli bir rol oynamaktadır. Bu çalışmada uzun kısa süreli bellek (LSTM) sinir ağı, uyarlanabilir nöro-bulanık çıkarım sistemi (ANFIS) ile bulanık c-ortalamalar (FCM) ve ızgara bölümü (GP) gibi üç farklı veri odaklı model, bir gün sonrasının tahminini gerçekleştirmek için kullanılmıştır. Analizler, 2014-2018 yılları arasında Türkiye Devlet Meteoroloji İşleri tarafından Çanakkale ilinde ölçülen 5 yıllık günlük ortalama SWT'ler kullanılarak gerçekleştirilmiştir. Ölçülen veriler ayrıca önerilen modeller tarafından üretilen verileri doğrulamak için kullanılmıştır. Önerilen modeller için performans kriterleri, ortalama mutlak hata (MAE), ortalama kare hata (RMSE) ve korelasyon katsayısıdır (R). ANFIS-FCM tekniği ile günlük SWT tahminine göre MAE, RMSE ve R değerleri için en iyi sonuçlar sırasıyla 0,113oC, 0,191oC ve 0,9994 olarak elde edilmiştir.

Keywords

LSTM, ANFIS, Deniz suyu sıcaklığı, Güç santrali

Daily Sea Water Temperature Forecasting Using Machine Learning Approaches

Abstract

The efficiency of turbines in seaside nuclear or coal-fired power plants is directly proportional to sea water temperature (SWT). The cooling medium temperature is critical in the design of any power plant when considering long-term average climatic conditions. As a result, the deviation in the SWT affects the efficiency of electricity generation. Accurate SWT estimation is critical for electrical output from power plant applications in this regard. Three different data-driven models such as long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) and grid partition (GP) were used to perform one-day ahead short-term SWT prediction, in this paper. The analyses were performed using 5-year daily mean SWTs measured by the Turkish State Meteorological Service in Canakkale Province between 2014 and 2018. The measured data was also used to validate the data produced by the proposed techniques. Performance criteria for the techniques suggested are mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R). With the ANFIS-FCM technique, the best outcomes for MAE, RMSE and R values were obtained as 0.113oC, 0.191oC, and 0.9994, respectively, according to daily SWT forecasting.

Keywords

LSTM, ANFIS, Sea water temperature, Power plant

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