A DEEP LEARNING-BASED DEMAND FORECASTING SYSTEM FOR PLANNING ELECTRICITY GENERATION

Muhammet Mustafa Gökçe; Erkan Duman

doi:10.17780/ksujes.1399160

EN TR

A DEEP LEARNING-BASED DEMAND FORECASTING SYSTEM FOR PLANNING ELECTRICITY GENERATION

Abstract

In today's world, where economic and industrial development continues, the importance of electrical energy is constantly increasing. Energy demand should be forecast as precisely as possible to reduce lost energy costs in the system, to plan generation expenditures appropriately, to ensure that market players are not economically harmed, and to deliver quality and uninterrupted energy to system consumers. Balancing the electric energy supply and demand of the system is possible with a forecasting plan. Our research aims to generate hourly electricity consumption load forecasts for the period 2018-2021 using Turkish Electricity Consumption Data and meteorological data, with the addition of time and public holiday features. The forecasting performance of the models is evaluated by training multiple machine learning models and deep neural network-based time series models with the data. When the prediction results of our load demand forecasting problem were evaluated, it was seen that deep learning methods gave higher results in prediction success compared to machine learning models. It has been observed that the prediction success of the LSTM model, one of the deep learning methods we use, is higher than the RNN and GRU models. The analysis envisages the elimination of mismatches between energy supply and demand.

Keywords

ELEKTRİK ÜRETİMİNİN PLANLANMASI İÇİN DERİN ÖĞRENME TABANLI TALEP TAHMİN SİSTEMİ

Öz

Ekonomik ve endüstriyel gelişimin devam ettiği günümüz dünyasında elektrik enerjisinin önemi sürekli artmaktadır. Sistemdeki kayıp enerji maliyetlerini azaltmak, üretim harcamalarını uygun şekilde planlamak, piyasa oyuncularının ekonomik olarak zarar görmemesini sağlamak ve sistem tüketicilerine kaliteli ve kesintisiz enerji ulaştırmak için enerji talebinin mümkün olduğunca hassas bir şekilde tahmin edilmesi gerekmektedir. Sistemin elektrik enerjisi arz ve talebinin dengelenmesi bir tahmin planı ile mümkündür. Araştırmamız, Türkiye Elektrik Tüketim Verileri ve meteorolojik veriler kullanılarak, zaman ve resmi tatil özellikleri de eklenerek 2018-2021 dönemi için saatlik elektrik tüketim yük tahminleri üretmeyi amaçlamaktadır. Modellerin tahmin performansı, çoklu makine öğrenimi modelleri ve derin sinir ağı tabanlı zaman serisi modelleri verilerle eğitilerek değerlendirilmektedir. Yük talep tahmin problemimizinin tahmin sonuçları değerlendirildiğinde derin öğrenme yöntemlerinin makine öğrenmesi modellerine kıyasla tahmin başarısında daha yüksek sonuçlar verdiği görülmüştür. Kullandığımız derin öğrenme yöntemlerinden LSTM modelinin tahmin başarısının RNN ve GRU modellerinden daha yüksek olduğu gözlemlenmiştir. Analiz, enerji arzı ve talebi arasındaki uyumsuzlukların giderilmesini öngörmektedir.

Anahtar Kelimeler

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Details

Primary Language

English

Subjects

Deep Learning

Journal Section

Research Article

Authors

Muhammet Mustafa Gökçe ^*
0000-0003-1214-1698
Türkiye

Erkan Duman
0000-0003-2439-7244
Türkiye

Publication Date

June 3, 2024

Submission Date

December 1, 2023

Acceptance Date

March 26, 2024

Published in Issue

Year 2024 Volume: 27 Number: 2

DOI

https://doi.org/10.17780/ksujes.1399160

IZ

https://izlik.org/JA55LD92PC

Cite

RIS / Bibtex

APA

Gökçe, M. M., & Duman, E. (2024). A DEEP LEARNING-BASED DEMAND FORECASTING SYSTEM FOR PLANNING ELECTRICITY GENERATION. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 511-522. https://doi.org/10.17780/ksujes.1399160

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