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Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2022 / Cilt: 22 - Sayı: 5

Feature Selection by Genetic Algorithm for Wind Power Prediction

Rüzgar Gücü Tahmininde Genetik Algoritma ile Öznitelik Seçimi

Ece ÇETİN YAĞMUR
Sercan YAĞMUR
Pages
1028–1040
Publication Date
2022-10-27
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In the final model proposed by GA, the number of variables was reduced from 47 to 9, unnecessary variables were removed from the model, and a strong prediction model with R2 value of 0.98 was obtained with the least number of variables.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The need for renewable energy sources for sustainable development has been increasing every day.
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Key Findings & Data:In this study, wind power estimation was carried out for a wind turbine in Turkey, using the data set obtained by the SCADA system during 2018 and the meteorological data set shared by NASA for the same location.
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Conclusion & Impact:In the final model proposed by GA, the number of variables was reduced from 47 to 9, unnecessary variables were removed from the model, and a strong prediction model with R2 value of 0.98 was obtained with the least number of variables.
🔬Design:Modeling & Simulation
📍Scope:2018 Verileri
⚙️Variables:Makine öğrenmesi · Rüzgar gücü · Yenilenebilir enerji

Abstract

The need for renewable energy sources for sustainable development has been increasing every day. One of these sources is wind energy. Due to the stochastic nature of the wind, the estimation of wind speed and wind power has been a subject of great interest to researchers in recent years. In this study, wind power estimation was carried out for a wind turbine in Turkey, using the data set obtained by the SCADA system during 2018 and the meteorological data set shared by NASA for the same location. Wind speed, wind direction, and theoretical power curve were taken from the SCADA system as input variables; Meteorological parameters were taken from the NASA system and historical data of wind power were used. Unnecessary features in the model that cause computational complexity are removed from the model with the wrapper selection method to increase model performance. Genetic Algorithm (GA) was used as the wrapper selection method. In the study, the predictive power of different machine learning algorithms was compared according to different performance criteria and the effect of feature selection on the model was evaluated. In the final model proposed by GA, the number of variables was reduced from 47 to 9, unnecessary variables were removed from the model, and a strong prediction model with R2 value of 0.98 was obtained with the least number of variables.

Özet

Sürdürülebilir gelişim için yenilenebilir enerji kaynaklarına olan ihtiyaç her geçen gün artmaktadır. Bu kaynaklardan birisi de rüzgar enerjisidir. Rüzgarın stokastik yapısı nedeniyle rüzgar hızı ve rüzgar gücünün tahmini son yıllarda araştırmacılar tarafından oldukça ilgi çeken bir konu haline gelmiştir. Yapılan çalışmada Türkiye’de yer alan bir rüzgar türbini için 2018 yılı boyunca SCADA sistemi ile elde edilen veri seti ile aynı konum için NASA tarafından paylaşılan meteorolojik veri seti kullanılarak rüzgar gücü tahmini gerçekleştirilmiştir. Girdi değişkenleri olarak SCADA sisteminden çekilen rüzgar hızı, rüzgar yönü ve teorik güç eğrisi; NASA sisteminden çekilen meteorolojik parametreler ve rüzgar gücüne ait geçmiş veriler kullanılmıştır. Modelde yer alan ve hesaplama karmaşıklığına neden olan gereksiz öznitelikler model performansını artırmak amacıyla sarmal seçim yöntemi ile modelden çıkarılmıştır. Sarmal seçim yöntemi olarak Genetik Algoritma (GA) kullanılmıştır. Yapılan çalışmada hem farklı makine öğrenme algoritmalarının tahmin gücü, farklı performans ölçütlerine göre karşılaştırılmış hem de öznitelik seçiminin modele etkisi değerlendirilmiştir. GA ile önerilen nihai modelde değişken sayısı 47’den 9’a indirgenerek gereksiz değişkenler modelden uzaklaştırılmış ve en az sayıda değişken ile R2 değeri 0,98 olan güçlü bir tahmin modeli elde edilmiştir.

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Cite This Article

YAĞMUR, E. Ç., YAĞMUR, S. (2022). Feature Selection by Genetic Algorithm for Wind Power Prediction. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 22 - Sayı: 5, 1028–1040. https://ajindex.com/articles/874221

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