Forecasting National Electricity Consumption Using Machine Learning Methods: An Application with UK Data

Öz

Forecasting electricity demand is vital for operating energy systems in a secure, economical, and environmentally sustainable manner. Recently, studies on electricity demand forecasting methods have focused on improving classical methods and integrating new technologies. This study aimed to forecast electricity demand using the electricity demand dataset from the United Kingdom. For this purpose, we utilized machine learning methods such as Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost) and Boosted Linear Regression using the Python programming language in the Google Colab environment. We compared the method performances using accuracy measurement criteria such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results demonstrate the superiority of the LSTM method over the other methods.

Anahtar Kelimeler

• Electricity Demand Forecasting
• LSTM
• XGBoost
• Boosted Linear Regression

ULUSAL ELEKTRİK TÜKETİMİNİN MAKİNE ÖĞRENMESİ YÖNTEMLERİYLE TAHMİN EDİLMESİ: BİRLEŞİK KRALLIK VERİLERİYLE BİR UYGULAMA

Öz

Elektrik enerjisi talebinin tahmin edilmesi, enerji sistemlerinin güvenli, ekonomik ve çevresel olarak sürdürülebilir bir şekilde işletilmesi için hayati öneme sahiptir. Son zamanlarda, elektrik talep tahmin yöntemleri üzerine yapılan çalışmalar hem klasik yöntemlerin iyileştirilmesi hem de yeni teknolojilerin entegrasyonu üzerine yoğunlaşmıştır. Bu çalışmada, Birleşik Krallık'taki elektrik talebi veri seti kullanılarak elektrik talebinin tahmin edilmesi amaçlanmıştır. Bu amaç doğrultusunda, Uzun Kısa Süreli Bellek (LSTM), Aşırı Gradyan Artırma Algoritması (XGBoost) ve Lineer Artırma Regresyonu gibi çeşitli tahmin modelleri Google Colab ortamında Python yazılım dili kullanılarak uygulanmıştır. Modellerin performansları, Kök Ortalama Kare Hata (RMSE) ve Ortalama Mutlak Yüzde Hata (MAPE) doğruluk ölçüm yöntemleri kullanılarak karşılaştırılmıştır. Çalışmanın sonuçları LSTM yönteminin diğer yöntemlere üstünlüğünü göstermektedir.

Anahtar Kelimeler

• Elektrik Talebi Tahmini
• LSTM
• XGBoost
• Artırımlı Lineer Regresyon

Kaynakça

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