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Dergiler / Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics / 2025 / Volume: 74 Issue: 4

Univariate deep learning models for short-term electricity load forecasting from renewables

Fatma Başoğlu Kabran(İzmir Yüksek Teknoloji Enstitüsü)0000-0002-0212-5785
Kamil Demirberk Ünlü(ATILIM ÜNİVERSİTESİ)0000-0002-2393-6691
Sayfa
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Yayın Tarihi
2025-12-24
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

This research shows that the univariate model based on historical data of electricity load generated from renewables can accurately predict day-ahead electricity load, even without meteorological data.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Renewable energy offers a cost-effective, carbon-free solution for energy needs, while protecting the environment.
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Temel Bulgular & Veri:Accurate forecasting of electricity generation from renewable sources is crucial for the efficiency of modern power grids.
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Sonuç & Katkı:This research shows that the univariate model based on historical data of electricity load generated from renewables can accurately predict day-ahead electricity load, even without meteorological data.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Deep learning · univariate model · renewable energy

Abstract

Renewable energy offers a cost-effective, carbon-free solution for energy needs, while protecting the environment. Accurate forecasting of electricity generation from renewable sources is crucial for the efficiency of modern power grids. This study employs a univariate deep learning approach to predict daily renewable energy generation, evaluating Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) as candidate models. Five performance metrics—mean absolute error, root mean squared error, mean absolute percentage error, mean absolute scaled error and the coefficient of determination—are employed to assess the forecasting power of the algorithms. The empirical results show that CNN outperforms other models, achieving an $R^2$ of almost $94\%$. This research shows that the univariate model based on historical data of electricity load generated from renewables can accurately predict day-ahead electricity load, even without meteorological data.

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Bu Makaleye Atıf Yap

Kabran, F. B., Ünlü, K. D. (2025). Univariate deep learning models for short-term electricity load forecasting from renewables. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, Volume: 74 Issue: 4. https://doi.org/10.31801/cfsuasmas.1643466

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