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Journals / International Journal of Energy and Smart Grid / 2026 / Volume: 10 Issue: 1

MEDIUM-TERM RESIDENTIAL ENERGY CONSUMPTION PREDICTION USING TCN AND TCN-LSTM MODELS

Mihriban Günay(MUNZUR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0002-0932-1981
Özal Yıldırım(FIRAT ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, YAZILIM MÜHENDİSLİĞİ BÖLÜMÜ, YAZILIM MÜHENDİSLİĞİ PR.)0000-0001-5375-3012
Yakup Demir(FIRAT ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ PR.)0000-0001-9530-5824
Pages
—
Publication Date
2025-05-30
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AI Summary & Key Takeaways

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

When the prediction performances of TCN and TCN-LSTM models are compared with the performances of other prediction algorithms, it is seen that they have lower error rates and are more successful in medium-term electricity consumption prediction.

📌 3 Key Takeaways

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Objective & Hypothesis:: Prediction of electricity consumption is important for energy management and efficient use of energy resources.
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Key Findings & Data:Accurate prediction of electricity consumption provides benefits to consumers and distribution companies in terms of correct management of electricity demand, electricity usage cost and waste control.
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Conclusion & Impact:When the prediction performances of TCN and TCN-LSTM models are compared with the performances of other prediction algorithms, it is seen that they have lower error rates and are more successful in medium-term electricity consumption prediction.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Electricity consumption prediction · TCN · TCN-LSTM

Abstract

: Prediction of electricity consumption is important for energy management and efficient use of energy resources. Accurate prediction of electricity consumption provides benefits to consumers and distribution companies in terms of correct management of electricity demand, electricity usage cost and waste control. This article proposes Temporal Convolutional Network (TCN) and Temporal Convolutional Network with Long Short-Term Memory (TCN-LSTM) models for medium-term electricity consumption prediction with an open access dataset in individual household electricity consumption prediction. An individual household’s 5000-hour electricity consumption prediction is made with the proposed models. The performance of the proposed models is compared with different prediction algorithms. The performance of all models used in the study is evaluated with three evaluation metrics commonly used in prediction model performance evaluation. When the prediction performances of TCN and TCN-LSTM models are compared with the performances of other prediction algorithms, it is seen that they have lower error rates and are more successful in medium-term electricity consumption prediction.

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Cite This Article

Günay, M., Yıldırım, Ö., Demir, Y. (2025). MEDIUM-TERM RESIDENTIAL ENERGY CONSUMPTION PREDICTION USING TCN AND TCN-LSTM MODELS. International Journal of Energy and Smart Grid, Volume: 10 Issue: 1. https://doi.org/10.55088/ijesg.1651188

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