A Deep Learning Approach to Real-time Electricity Load Forecasting

Year 2023, Volume: 5 Issue: 2, 1 - 9, 30.12.2023

Alaa Harith Mohammed Al-hamid , Serkan Savaş

https://doi.org/10.59940/jismar.1357804

Abstract

In light of the increasing importance of accurate and real-time electrical demand forecasting, this research presents a deep learning model with the goal of dramatically improving predictive accuracy. Conventional methods of forecasting, such as linear regression, have trouble capturing the complex patterns included in data about electricity usage. Standard machine learning methods are shown to be wanting when compared to the suggested deep Long Short-Term Memory (LSTM) model. Mean Absolute Error (MAE) of 5.454 and Mean Squared Error (MSE) of 18.243 demonstrate the deep LSTM model's proficiency in tackling this problem. The linear regression, on the other hand, achieved a MAE of 47.352 and an MSE of 65.606, which is lower than the proposed model. Because of its greater predictive precision and reliability, the deep LSTM model is a viable option for accurate, real-time prediction of electricity demand.

Keywords

Plan of array, Power flow, Deep learning, Load demand forecast, Stacked LSTM, Smart grids

Project Number

1

Gerçek Zamanlı Elektrik Yük Tahmini için Bir Derin Öğrenme Yaklaşımı

Abstract

Doğru ve gerçek zamanlı elektrik talebi tahmininin artan önemi ışığında, bu araştırma, tahmin doğruluğunu önemli ölçüde artırmak amacıyla bir derin öğrenme modeli sunmaktadır. Doğrusal regresyon gibi geleneksel tahmin yöntemleri, elektrik kullanımıyla ilgili verilerde yer alan karmaşık kalıpları yakalamakta zorlanmaktadır. Standart makine öğrenimi yöntemlerinin, önerilen derin Uzun Kısa Vadeli Bellek (Long Short-Term Memory-LSTM) modeliyle karşılaştırıldığında yetersiz kaldığı görülmüştür. Ortalama Mutlak Hata (MAE) 5.454 ve Ortalama Karesel Hata (MSE) 18.243, derin LSTM modelinin bu sorunun üstesinden gelmedeki yeterliliğini göstermektedir. Doğrusal regresyon ise 47.352 MAE değeri ve 65.606 MSE değeri ile önerilen modelden daha düşük başarı sonucu elde etmiştir. Daha yüksek tahmin hassasiyeti ve güvenilirliği nedeniyle, derin LSTM modeli elektrik talebinin doğru, gerçek zamanlı tahmini için uygun bir seçenektir.

Keywords

Dizi planı, Güç akışı, Derin öğrenme, Yük talep tahmini, Akıllı şebekeler, Yığın LSTM

Ethical Statement

i don't have

Supporting Institution

ÇANKIRI KARATEKIN UNIVERSITY

Project Number

1

Thanks

I would like to thank my thesis advisor, Assoc. Prof. Dr. Serkan SAVAŞ, for his patience, guidance and understanding.

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