DEEP LEARNING-DRIVEN SMART GRID OPTIMIZATION FOR SUSTAINABLE ENERGY EFFICIENCY IN SMART CITIES
DOI:
https://doi.org/10.69980/y0eya064Keywords:
: Deep Learning, Smart Grid Optimization, Smart CitiesAbstract
With the rising need for sustainable energy management and intelligent city infrastructure, the use of artificial intelligence and deep learning techniques in smart grid systems has been gaining traction. Energy optimization frameworks in smart cities need to be adaptive and data-driven, with the ability to optimally manage the dynamic electricity consumption patterns and to achieve sustainability goals. The present study was aimed at creating a deep learning based smart grid optimization framework to enhance sustainable energy efficiency and predictive energy management in smart city environment. The large-scale smart grid load forecasting dataset with 5 years of hourly electricity consumption data and environmental variables was used to perform the research quantitatively with data-driven research approach. Short term load forecasting and energy optimization using a deep learning model namely, Long Short-Term Memory (LSTM) is done after data preprocessing, feature engineering, and time-series analysis. The developed framework was tested with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to measure the predictive performance and forecasting accuracy of the developed framework. It was found that the proposed deep learning model was able to capture the temporal energy consumption patterns and delivered good forecasting performance under different operational and seasonal conditions. It substantially enhanced the energy scheduling, energy load balancing efficiency, and intelligent resource allocation in the smart grid environment by utilizing predictive analytics. The findings also showed that AI forecasting models could aid in the integration of renewable energy, decrease operational losses and improve the sustainability performance of smart city systems. The study revealed that deep learning-based predictive analytics is a viable and scalable solution to optimize smart grid for intelligence and promote sustainable urban energy management. The framework proposed here will be helpful in the future development of intelligent urban infrastructure and will provide valuable inputs to the current research on applications of artificial intelligence, sustainable engineering and smart city energy systems.
References
1. Agupugo, C. P., Barrie, I., Makai, C. C., & Alaka, E. (2024). AI learning-driven optimization of microgrid systems for rural electrification and economic empowerment. Engineering Science & Technology Journal, 5(9), 2835-2851.
2. Ahmed, S. R., Hussain, A. S. T., Majeed, D. A., Jghef, Y. S., Tawfeq, J. F., Taha, T. A., ... & Ahmed, O. K. (2024, January). Machine Learning for Sustainable Power Systems: AIoT-Optimized Smart-Grid Inverter Systems with Solar Photovoltaics. In International Conference on Forthcoming Networks and Sustainability in the AIoT Era (pp. 368-378). Cham: Springer Nature Switzerland.
3. Ahsan, F., Dana, N. H., Sarker, S. K., Li, L., Muyeen, S. M., Ali, M. F., ... & Das, P. (2023). Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review. Protection and Control of Modern Power Systems, 8(3), 1-42.
4. Al Montaser, M. A., & Bhuiyan, M. A. I. (2025). Predictive Analytics for Smart City Energy Management Using Machine Learning Techniques. Frontiers in Computer Science and Artificial Intelligence, 4(4), 71-82.
5. Aljohani, A. (2024). Deep learning-based optimization of energy utilization in IoT-enabled smart cities: A pathway to sustainable development. Energy Reports, 12, 2946-2957.
6. Al-Qarafi, A., Alsolai, H., Alzahrani, J. S., Negm, N., Alharbi, L. A., Al Duhayyim, M., ... & Al-Wesabi, F. N. (2022). Artificial jellyfish optimization with deep-learning-driven decision support system for energy management in smart cities. Applied Sciences, 12(15), 7457.
7. Asadi, S., Naeini, H. K., Hassanlou, D., Pishahang, A., Najafabadi, S. A., Sharifi, A., & Ahmadi, M. (2025). AI-Powered Digital Twin Frameworks for Smart Grid Optimization and Real-Time Energy Management in Smart Buildings: A Survey. Computer Modeling in Engineering & Sciences (CMES), 145(2).
8. Cicceri, G., Tricomi, G., D’Agati, L., Longo, F., Merlino, G., & Puliafito, A. (2023). A deep learning-driven self-conscious distributed cyber-physical system for renewable energy communities. Sensors, 23(9), 4549.
9. Emperor Graphics. (2025). Smart grid load forecasting dataset (5-year hourly) [Data set]. Kaggle. https://www.kaggle.com/datasets/emperorgraphics/hourly-load-consumption-data
10. Kamble, A. G., Ganesan, P., Hameed, T., Maruthakutti, M., Beeravelly, S. R., & Rajalakshmi, S. (2025, December). Reinforcement Learning-Driven Smart Energy Management for Carbon-Neutral Smart Cities. In 2025 4th International Conference on Applied Artificial Intelligence and Computing (ICAAIC) (pp. 599-606). IEEE.
11. Kumar, H., Tshakwanda, P. M., & Devetsikiotis, M. (2025, May). AI-Driven Smart Grid Optimization: Enhancing Urban Communication Networks. In 2025 IEEE World AI IoT Congress (AIIoT) (pp. 0273-0279). IEEE.
12. Kumar, S. S., Usha, P., Balakrishnan, P., Kannan, V. K., Manjula, M., & Vijayakumar, M. (2024, November). Deep Learning Driven Engineering Innovations: Advancing Sustainable Green Building and Smart Infrastructure for a Resilient Future. In 2024 First International Conference for Women in Computing (InCoWoCo) (pp. 1-5). IEEE.
13. Malik, S., & Ali, M. (2024). Machine Learning–Driven Optimization Techniques for Intelligent Decision-Making Systems. International Journal of Data Sciences and Intelligent Systems, 1(01), 1-16.
14. Mamadiyarov, Z., Sivaraman, P. R., Kumar, N. M. G., & Singh, P. P. (2025, July). A Deep Learning-Driven Framework for Sustainable and Intelligent Energy Management in Smart Cities. In 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) (pp. 1-7). IEEE.
15. Miraftabzadeh, S. M., Di Martino, A., Longo, M., & Zaninelli, D. (2024). Deep learning in power systems: A bibliometric analysis and future trends. IEEE Access, 12, 163172-163196.
16. Prakash, N. (2025, November). Machine Learning-Driven IoT Framework for Dynamic Street Lighting Management and Energy Optimization. In 2025 5th International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN) (pp. 1317-1321). IEEE.
17. Rajput, M., & Yadav, R. N. (2025). Machine and deep learning driven energy efficient clustering in IoT-WSNs: A Review. IEEE Sensors Journal.
18. Reza, S. A., Hasan, M. S., Amjad, M. H. H., Islam, M. S., Rabbi, M. M. K., Hossain, A., ... & Jakir, T. (2025). Predicting energy consumption patterns with advanced machine learning techniques for sustainable urban development. Journal of Computer Science and Technology Studies, 7(1), 265-282.
19. Rojek, I., Mikołajewski, D., Galas, K., & Piszcz, A. (2025). Advanced deep learning algorithms for energy optimization of smart cities. Energies, 18(2), 407.
20. Tong, Z., Zhou, Y., & Xu, K. (2023). An intelligent scheduling control method for smart grid based on deep learning. Math. Biosci. Eng, 20(5), 7679-7695.
21. Waghmare, S. K. R., & Dube, R. R. (2024, November). Comprehensive Review on Deep Learning-Based Approach for Electricity Consumption Prediction. In International Conference on Cognitive and Intelligent Computing (pp. 351-356). Singapore: Springer Nature Singapore.
22. Wen, X., Liao, J., Niu, Q., Shen, N., & Bao, Y. (2024). Deep learning-driven hybrid model for short-term load forecasting and smart grid information management. Scientific reports, 14(1), 13720.
23. Zhao, C., Wu, X., Hao, P., Wang, Y., & Zhou, X. (2024). Machine learning for optimal net-zero energy consumption in smart buildings. Sustainable Energy Technologies and Assessments, 64, 103664.
