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

Year 2025, Volume: 10 Issue: 1, 11 - 18, 30.05.2025

Mihriban Günay , Özal Yıldırım , Yakup Demir

https://doi.org/10.55088/ijesg.1651188

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.

Keywords

Electricity consumption prediction, TCN, TCN-LSTM

Ethical Statement

The authors declare that this document does not require ethics committee approval or any special permission. This review does not cause any harm to the environment and does not involve the use of animal or human subjects.

References

• Lian, H., Ji, Y., Niu, M., Gu, J., Xie, J., Liu, J., “A hybrid load prediction method of office buildings based on physical simulation database and LightGBM algorithm”, Applied Energy, 377, 124620, 2025.
• Kim, T.Y., Cho, S.B., “Predicting residential energy consumption using CNN-LSTM neural networks”, Energy, 182, 72–81, 2019.
• Cao, W., et al., “Short-term energy consumption prediction method for educational buildings based on model integration”, Energy, 283, 128580, 2023.
• Jana, R.K., Ghosh, I., Sanyal, M.K., “A granular deep learning approach for predicting energy consumption”, Applied Soft Computing, 89, 106091, 2020.
• Ma, Z., Ye, C., Ma, W., “Support vector regression for predicting building energy consumption in southern China”, Energy Procedia, 3433–3438, 2019.
• Gaur, K., Singh, S.K., “CNN-Bi-LSTM based household energy consumption prediction”, Proceedings of the 2021 3rd International Conference on Signal Processing and Communication (ICPSC), pp. 233–237, 2021.
• Frikha, M., Taouil, K., Fakhfakh, A., Derbel, F., “Predicting power consumption using deep learning with stationary wavelet”, Forecasting, 6(3), 864–884, 2024.
• Ahmed, A., et al., “Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption”, PLoS One, 20(1), 0315668, 2025.
• Wang, X., Zhao, T., Liu, H., He, R., “Power consumption predicting and anomaly detection based on long short-term memory neural network”, Proceedings of the 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), 2019.
• Bouderraoui, H., Chami, S., Ranganathan, P., “Predicting hourly energy consumption in buildings”, Proceedings of the IEEE International Conference on Electro Information Technology (EIT), pp. 323–329, 2021.
• Le, T., Vo, M.T., Kieu, T., Hwang, E., Rho, S., Baik, S.W., “Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building”, Sensors, 20(9), 2020.
• Mahjoub, S., Chrifi-Alaoui, L., Marhic, B., Delahoche, L., “Predicting energy consumption using LSTM, multi-layer GRU and Drop-GRU neural networks”, Sensors, 22(11), 2022.
• Lecun, Y., Bengio, Y., Hinton, G., “Deep learning”, Nature, 521(7553), 436–444, 2015