Short-Term Electricity Load Forecasting Using Hybrid CNN–LSTM Models on the UCI Electricity Load Dataset
Keywords:
Short-Term Load, CNN–LSTM, Hybrid Forecasting, Electricity Prediction, Energy Management.Abstract
Accurate short-term electricity load forecasting (STLF) is critical for efficient energy management, demand response, and grid stability in modern residential environments. This study presents an empirical investigation of a hybrid convolutional neural network–long short-term memory (CNN–LSTM) model applied to the UCI Electricity Load dataset, integrating convolutional layers to extract localized temporal features and stacked LSTM layers to model long-term dependencies across households. The model is trained using Python and TensorFlow on a GPU-enabled workstation, with preprocessing including normalization, sliding-window sequence generation, and train-validation-test splitting. Performance is evaluated through mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and k-fold cross-validation. Comparative benchmarking against LSTM, CNN-GRU, and multi-scale CNN-LSTM architectures demonstrates superior accuracy, stability, and generalizability of the proposed hybrid model. Sensitivity and interpretability analyses further reveal critical temporal patterns, feature contributions, and operational insights, facilitating actionable energy management decisions. These results substantiate the hybrid CNN–LSTM approach as a robust, interpretable, and operationally relevant solution for STLF applications.
Downloads
References
Alanazi, M. D., Saeed, A., Islam, M., Habib, S., Sherazi, H. I., Khan, S., & Shees, M. M. (2023). Enhancing short-term electrical load forecasting for sustainable energy management in low-carbon buildings. Sustainability, 15(24), 16885.
Alhussein, M., Aurangzeb, K., & Haider, S. I. (2020). Hybrid CNN-LSTM model for short-term individual household load forecasting. Ieee Access, 8, 180544-180557.
Al-Saudi, K., Degeler, V., & Medema, M. (2021). Energy consumption patterns and load forecasting with profiled CNN-LSTM networks. Processes, 9(11), 1870.
Alsharekh, M. F., Habib, S., Dewi, D. A., Albattah, W., Islam, M., & Albahli, S. (2022). Improving the efficiency of multistep short-term electricity load forecasting via R-CNN with ML-LSTM. Sensors, 22(18), 6913.
Chinnaraji, R., & Ragupathy, P. (2022). Accurate electricity consumption prediction using enhanced long short‐term memory. IET Communications, 16(8), 830-844.
Guo, X., Zhao, Q., Zheng, D., Ning, Y., & Gao, Y. (2020). A short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price. Energy Reports, 6, 1046-1053.
Han, J., & Zeng, P. (2025). Residual BiLSTM based hybrid model for short-term load forecasting in buildings. Journal of Building Engineering, 99, 111593.
Hasanat, S. M., Ullah, K., Yousaf, H., Munir, K., Abid, S., Bokhari, S. A. S., ... & Ullah, Z. (2024). Enhancing short-term load forecasting with a CNN-GRU hybrid model: A comparative analysis. IEEE Access, 12, 184132-184141.
Ishaq, M., & Kwon, S. (2021). Short-term energy forecasting framework using an ensemble deep learning approach. IEEE Access, 9, 94262-94271.
Jogunola, O., Adebisi, B., Hoang, K. V., Tsado, Y., Popoola, S. I., Hammoudeh, M., & Nawaz, R. (2022). CBLSTM-AE: a hybrid deep learning framework for predicting energy consumption. Energies, 15(3), 810.
Khan, Z. A., Hussain, T., Ullah, A., Rho, S., Lee, M., & Baik, S. W. (2020). Towards efficient electricity forecasting in residential and commercial buildings: A novel hybrid CNN with a LSTM-AE based framework. Sensors, 20(5), 1399.
Khan, Z. A., Ullah, A., Ullah, W., Rho, S., Lee, M., & Baik, S. W. (2020). Electrical energy prediction in residential buildings for short-term horizons using hybrid deep learning strategy. Applied Sciences, 10(23), 8634.
Le, T., Vo, M. T., Vo, B., Hwang, E., Rho, S., & Baik, S. W. (2019). Improving electric energy consumption prediction using CNN and Bi-LSTM. Applied Sciences, 9(20), 4237.
Mounir, N., Ouadi, H., & Jrhilifa, I. (2023). Short-term electric load forecasting using an EMD-BI-LSTM approach for smart grid energy management system. Energy and Buildings, 288, 113022.
Naz, A., Javed, M. U., Javaid, N., Saba, T., Alhussein, M., & Aurangzeb, K. (2019). Short-term electric load and price forecasting using enhanced extreme learning machine optimization in smart grids. Energies, 12(5), 866.
Özen, S., Yazıcı, A., & Atalay, V. (2025). Hybrid deep learning models with data fusion approach for electricity load forecasting. Expert Systems, 42(2), e13741.
Rani, S., Ahmed, U., Mahmood, A., Manzoor, S., Razzaq, S., Jamil, U., ... & Benbouzid, M. (2026). Dual Attention‐Empowered Bidirectional Long Short‐Term Memory Network for Short‐Term Electric Load Forecasting. Energy Technology, 14(1), e202500914.
Sajjad, M., Khan, Z. A., Ullah, A., Hussain, T., Ullah, W., Lee, M. Y., & Baik, S. W. (2020). A novel CNN-GRU-based hybrid approach for short-term residential load forecasting. Ieee Access, 8, 143759-143768.
Sajjad, M., Khan, Z. A., Ullah, A., Hussain, T., Ullah, W., Lee, M. Y., & Baik, S. W. (2020). A novel CNN-GRU-based hybrid approach for short-term residential load forecasting. Ieee Access, 8, 143759-143768.
Simaiya, S., Dahiya, M., Tomar, S., Faujdar, N., Sharma, Y. K., & Nuristani, N. (2024). A transfer learning-based hybrid model with LightGBM for smart grid short-term energy load prediction. Energy Exploration & Exploitation, 42(5), 1853-1876.
Singhal, D., Ahuja, L., & Seth, A. (2024). Forecasting smart grid stability using a hybrid CNN bi-LSTM approach. SN Computer Science, 5(5), 539.
Sinha, A., Tayal, R., Vyas, A., Pandey, P., & Vyas, O. P. (2021). Forecasting electricity load with hybrid scalable model based on stacked non linear residual approach. Frontiers in Energy Research, 9, 720406.
Ullah, F. U. M., Ullah, A., Haq, I. U., Rho, S., & Baik, S. W. (2019). Short-term prediction of residential power energy consumption via CNN and multi-layer bi-directional LSTM networks. IEEE Access, 8, 123369-123380.
Ullah, K., Ahsan, M., Hasanat, S. M., Haris, M., Yousaf, H., Raza, S. F., ... & Ullah, Z. (2024). Short-term load forecasting: A comprehensive review and simulation study with CNN-LSTM hybrids approach. Ieee Access, 12, 111858-111881.
Wu, M. P., & Wu, F. (2024). Predicting residential electricity consumption using CNN-BiLSTM-SA neural networks. IEEE Access, 12, 71555-71565.
