Hybrid Deep Learning Models for Energy Consumption Forecasting: A CNN-LSTM Approach for Large-Scale Datasets
DOI:
https://doi.org/10.69650/rast.2025.261326Keywords:
Electricity Consumption Forecasting, Smart Grids, Deep Learning, Hybrid CNN-LSTM Model, Energy ManagementAbstract
Long-term electricity consumption forecasting is essential with the rise of smart grids and advanced metering infrastructures for optimized energy management. Categorizing energy consumption into historical time series enables accurate predictions and deeper insights into energy trends. Deep learning has driven innovative forecasting models, particularly in smart grids leveraging data-driven approaches. This study applies convolutional neural networks (CNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks to analyze complex energy consumption patterns. Hybrid CNN-GRU and CNN-LSTM models are proposed to enhance forecasting accuracy by capturing spatial and temporal correlations. A comparative analysis is conducted against standalone GRU and LSTM models using historical data from American Electric Power (AEP) and Dominion Virginia Power (DOM). Performance is assessed using symmetric Mean Absolute Percentage Error (sMAPE), Loss, and Root Mean Square Error (RMSE). The findings demonstrate that hybridCNN-LSTMmodels improve forecasting accuracy, enabling proactive strategies like load shedding for efficient energy management as compared to other models. This research contributes to both academia and industry by enhancing smart grid reliability and operational efficiency.
References
Oliveira, M. C. Q. D., De Miranda, R. M., De Fátima Andrade, M. and Kumar, P., IMPACT OF URBAN GREEN AREAS ON AIR QUALITY: AN INTEGRATED ANALYSIS IN THE METROPOLITAN AREA OF SÃO PAULO. Environmental Pollution. 372 (2025) 126082, doi: https://doi.org/10.1016/j.envpol.2025.126082.
Li, G., Li, Y., Han, C., Jiang, C., Geng, M., Guo, N. and et al., Forecasting and Analyzing Influenza Activity in Hebei Province, China, Using a CNN-LSTM Hybrid Model. BMC Public Health. 24 (2024) 1-19, doi: https://doi.org/10.1186/s12889-024-19590-8.
Boucetta, L. N., Amrane, Y., Chouder, A., Arezki, S. and Kichou, S., Enhanced forecasting accuracy of a grid connected photovoltaic power plant: A novel approach using hybrid variational mode decomposition and a CNN LSTM model. Energies. 17 (2024) 1781, doi: https://doi.org/10.3390/en17071781.
Zhang, Y., Zhou, Z., Van Griensven Thé, J., Yang, S. X. and Gharabaghi, B., Flood forecasting using hybrid LSTM and GRU models with lag time preprocessing. Water. 15 (2023) 3982, doi: https://doi.org/10.3390/w15223982.
Belletreche, M., Bailek, N., Abotaleb, M., Bouchouicha, K., Zerouali, B., Guermoui, M., et al., Hybrid Attention-Based Deep Neural Networks for Short-Term Wind Power Forecasting Using Meteorological Data in Desert Regions. Scientific Reports. 14 (2024) 1-17, doi: https://doi.org/10.1038/s41598-024-73076-6.
Cao, K., Zhang, T. and Huang, J., Advanced Hybrid LSTM-Transformer Architecture for Real-Time Multi-Task Prediction in Engineering Systems. Scientific Reports. 14 (2024) 1-24, doi: https://doi.org/10.1038/s41598-024-55483-x.
Saleem, M. U., Shakir, M., Usman, M. R., Bajwa, M. H. T., Shabbir, N., Ghahfarokhi, P. S. and Daniel, K., Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids. Energies. 16 (2023) 4835, doi: https://doi.org/10.3390/en16124835.
Vardhan, R. V., Vaishnavi, J., Shyamala, P., Siri, G. S. K. S. and Anand, R. Implementation of Demand Side Management Using PSO Algorithm. in 2023 Global Conference on Information Technologies and Communications (GCITC). (2023), 1–7, doi: https://doi.org/10.1109/GCITC60406.2023.
Kaya, M., Utku, A. and Canbay, Y., A hybrid CNN LSTM model for predicting energy consumption and production across multiple energy sources. Journal of Soft Computing and Artificial Intelligence. 5 (2024) 63-73, doi: https://doi.org/10.55195/jscai.1577431.
Rao, C., Sahoo, S. K. and Yanine, F., A systematic review of recent developments in IoT based demand side management for PV power generation. Energy Harvesting and Systems. 11 (2024), 20230124, doi: https://doi.org/10.1515/ehs-2023-0124.
Han, T., Muhammad, K., Hussain, T., Lloret, J. and Baik, S. W., An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks. IEEE Internet of Things Journal. 8 (2021) 3170–3179, doi: https://doi.org/10.1109/JIOT.2020.3013306.
dos Santos, S. I., da Silveira, D. S., da Costa, M. F. and de Freitas, H. M. S., Systematic review of sustainable energy consumption from consumer behavior perspective. Renewable and Sustainable Energy Reviews. 203 (2024) 114736, doi: https://doi.org/10.1016/j.rser.2024.114736.
Ku, T. Y., Park, W. K. and Choi, H., IoT Energy Management Platform for Microgrid. in 2017 IEEE 7th International Conference on Power and Energy Systems (ICPES). (2017), 106–110, doi: https://doi.org/10.1109/ICPESYS.2017.8215930.
Michailidis, P., Michailidis, I. and Kosmatopoulos, E., Review and evaluation of multi agent control applications for energy management in buildings. Energies. 17 (2024) 4835, doi: https://doi.org/10.3390/en17194835.
Liu, Y., Yang, C., Jiang, L., Xie, S. and Zhang, Y., Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities. IEEE Network. 33 (2019) 111–117, doi: http://dx.doi.org/10.1109/MNET.2019.1800254.
Cano, I. M. C. M., Hernández, G. A., Valverde, M. A. P. and Rodríguez, L. HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving. Energies. 13 (2020) 1097, doi: http://dx.doi.org/10.3390/en13051097.
Majee, A., Bhatia, M. and Swathika O. V., IoT Based Microgrid Automation for Optimizing Energy Usage and Controllability. in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). (2018), 685–689,doi: http://dx.doi.org/10.1109/ICECA.2018.8474789.
Pawar, P. and and K., Vittal P., Design and Development of Advanced Smart Energy Management System Integrated with IoT Framework in Smart Grid Environment. Journal of Energy Storage. 25 (2019) 100846, doi: http://dx.doi.org/10.1016/j.est.2019.100846.
Rashid, R. A., Chin, L., Sarijari, M. A., Sudirman, R. and Ide, T. Machine Learning for Smart Energy Monitoring of Home Appliances Using IoT. in 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN). (2019), 66–71, doi: https://doi.org/10.1109/ICUFN.2019.8806026.
Wu, J., Yang, B., Wang, L. and Park, J., Adaptive DRX Method for MTC Device Energy Saving by Using a Machine Learning Algorithm in an MEC Framework. IEEE Access. 9 (2021) 10548–10560, doi: https://doi.org/10.1109/ACCESS.2021.3049532.
Yaghmaee, M. H. and Hejazi, H. Design and Implementation of an Internet of Things Based Smart Energy Metering. in 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE). (2018), 191–194, doi: http://dx.doi.org/10.1109/SEGE.2018.8499458.
Yan, K., Wang, X., Du, Y., Jin, N., Huang, H. and Zhou, H., Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy. Energies. 11 (2018) 3089, doi: https://doi.org/10.3390/en11113089.
Namini, S. S., Tavakoli, N. and Namin, A.S. A Comparison of ARIMA and LSTM in Forecasting Time Series. in Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). (2018), 1394–1401, doi: http://dx.doi.org/10.1109/ICMLA.2018.00227.
Wang, K., Qi, X. and Liu, H., Photovoltaic Power Forecasting Based LSTM-Convolutional Network. Energy. 189 (2019) 116225, doi: https://doi.org/10.1016/j.energy.2019.116225.
Wang, J. Q., Du, Y. and Wang, J. LSTM Based Long-Term Energy Consumption Prediction with Periodicity. Energy. 197 (2020) 117197, doi: https://doi.org/10.1016/j.energy.2020.117197.
Yang, B., Yin, K., Lacasse, S. and Liu, Z., Time Series Analysis and Long Short-Term Memory Neural Network to Predict Landslide Displacement. Landslides. 16 (2019) 677–694, doi: https://doi.org/10.1007/s10346-018-01127-x.
Kim, K., Kim, D.K., Noh, J. and Kim, M., Stable Forecasting of Environmental Time Series via Long Short Term Memory Recurrent Neural Network. IEEE Access. 6 (2018) 75216–75228, doi: https://doi.org/10.1109/ACCESS.2018.2884827.
Heidari, A. and Khovalyg, D., Short-Term Energy Use Prediction of Solar-Assisted Water Heating System: Application Case of Combined Attention-Based LSTM and Time-Series Decomposition. Solar Energy. 207 (2020) 626–639, doi: https://doi.org/10.1016/j.solener.2020.07.008.
Tovar, M., Robles, M. and Rashid, F., PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case Study: Temixco-Morelos, Mexico. Energies.13 (2020) 6512, doi: https://doi.org/10.3390/en13246512.
Wu, L., Kong, C., Hao, X. and Chen, W., A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model. Mathematical Problems in Engineering. (2020) 1-10, doi: http://dx.doi.org/10.1155/2020/1428104.
Klungsida, N., Maneechot, P., Butploy, N. and Khiewwan, K., Forecasting Energy Consumption from EV Station Charging Using RNN, LSTM and GRU Neural Network. Journal of Renewable Energy and Smart Grid Technology. 19 (2024) 1–6, doi: https://doi.org/10.69650/rast.2024.254636.
Janthong, S. and Phukpattaranont, P., Recognition of multiple power quality disturbances based on discrete Wavelet transform and improved long Short-Term memory networks. Journal of Renewable Energy and Smart Grid Technology. 19 (2024) 7–25, doi: https://doi.org/10.69650/rast.2024.255814.
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