Machine learning based smart grid prediction models for enhancing environmental sustainability
Article Main
Abstract
The growing demand for electricity, driven by population growth and economic expansion, necessitates an efficient power distribution system and environmental sustainability. Smart Grids have emerged as a potential tool for enhancing the stability, reliability and sustainability of the grids. Machine learning models implemented in smart grids can predict consumer demand and improve smart grid performance. In this study, various machine learning models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forests, Gaussian Naïve Bayes, k-Nearest Neighbours, and Artificial Neural Networks, are used. The results of models with and without Principal Component Analysis integration are highlighted to predict the stability of smart grids. The metrics used in this study to evaluate model performance are accuracy, sensitivity, specificity, F1 score, and AUC-ROC. The results showed that machine learning models without principal component analysis performed better than those with principal component analysis on this dataset. The SVM-RBF kernel model with PCA achieved the highest accuracy of 80.39%, whereas the model without PCA achieved 98.15%. The results show that integrating PCA into ML models does not improve model performance; rather, it decreases accuracy, especially given the dataset’s size and the lower-dimensional feature space of the Smart Grid UCI dataset.
Article Details
Article Details
Keywords
Machine learning, Principal component analysis, Reliability, Smart grids, Stability
References
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Wang, Q., Yao, W., Fang, J., Ai, X., Wen, J., Yang, X., Xie, H. & Huang, X. (2020). Dynamic modeling and small signal stability analysis of distributed photovoltaic grid-connected system with large scale of panel level dc optimizers. Appl. Energy, 259, 114132.
You, S., Zhao, Y., Mandich, M., Cui, Y., Li, H., Xiao, H., Fabus, S., Su, Y., Liu, Y. & Yuan, H. (2020). A review on artificial intelligence for grid stability assessment. In Proceedings of the 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Tempe, AZ, USA, 11–13 November 2020, pp. 1–6.
Zahid, M., Ahmed, F., Javaid, N., Abbasi, R.A., Zainab Kazmi, H.S., Javaid, A., Bilal, M., Akbar, M. & Ilahi, M. (2019). Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics, 8, 122.
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S. & Wattam, S. (2020). Artificial Intelligence and machine learning approaches to energy demand-side response: A systematic review. Renew. Sustain. Energy Rev., 130, 109899.
Arzamasov, V., Böhm, K. & Jochem, P. (2018). Towards concise models of grid stability. In proceedings of the 2018 IEEE international conference on communications, control, and computing technologies for smart grids (SmartGridComm), Aalborg, Denmark, 29–31 October 2018, pp. 1–6.
Asaad, M., Ahmad, F., Alam, M.S. & Sarfraz, M. (2021). Smart grid and indian experience: a review. Resour. Policy, 74, 101499.
Bashir R. N., Mzoughi, O., Shahid, M. A., Alturki, N. & Saidani, O. (2024). Principal component analysis (PCA) and feature importance-based dimension reduction for reference evapotranspiration (ET0) predictions of Taif, Saudi Arabia. Computers and Electronics in Agriculture, 222, 109036. https://doi.org/10.1016/j.compag.2024.10 9036
Boopathy, P., Liyanage, M., Deepa, N., Velavali, M., Reddy, S., Maddikunta, P. K. R., Khare, N., Gadekallu, T. R., Hwang, W.J. & Pham, Q.V. (2024). Deep learning for intelligent demand response and smart grids: A comprehensive survey. Computer Science Review, 51, 100617.
Bose, B.K. (2017). Artificial intelligence techniques in smart grid and renewable energy systems-some example applications. Proc. IEEE, 105, 2262–2273.
Chen, J., Ooi, L. Q. R., Tan, T. W. K., Zhang, S., Li, J., Asplund, C. L., Eickhoff, S. B., Bzdok, D., Holmes, A. J. & Yeo, B. T. (2023). Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study. NeuroImage, 274, 20115.
Elizabeth, A. O., Oyaka, O. S., Orovwode, H., Airoboman, A. E., Gabriel, S. & Nwenenda, D. H. (2024). Identifying key reliability factors in micro-grid systems using principal component analysis. Journal Européen Des Systémes Automatisés, 57(1), 303.
Gauli, M.K., Phoungthong, K., Techato, K. & Gyawali, S. (2023). Predicting the stability of smart grid for improving the sustainability using distributed generation technology. e-Prime- Advances in Electrical Engineering, Electronics and Energy, 5, 100185. https://doi.org/10.1016/j.prime.2023.100185
Gunel, K. & Ekti, A.R. (2019). Exploiting machine learning applications for smart grids. In proceedings of the 2019 16th international multi-conference on systems, signals & devices (SSD), Istanbul, Turkey, 21–24 March 2019, pp. 679–685.
Hafeez, G., Khan, I., Jan, S., Shah, I. A., Khan, F. A. & Derhab, A. (2021). A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid. Applied Energy, 299, 117178.
Hong, Y., Zhou, Y., Li, Q., Xu, W. & Zheng, X. (2020). A deep learning method for short-term residential load forecasting in smart grid. IEEE Access, 8, 55785-55797.
Ibrar, M., Hassan, M.A., Shaukat, K., Alam, T.M., Khurshid, K.S., Hameed, I.A., Aljuaid, H. & Luo, S. (2022). A machine learning-based model for stability prediction of decentralized power grid linked with renewable energy resources. Wirel. Commun. Mob. Comput., 2022, 2697303.
Lahon, P., Kandali, A.B., Barman, U., Konwar, R.J., Saha, D. & Saikia, M.J. (2024). Deep neural network-based smart grid stability analysis: enhancing grid resilience and performance. Energies, 17, 2642. https://doi.org/10.3390/en17112642.
Li, X., Wang, Z., Yang, C. & Bozkurt, A. (2024). An advanced framework for net electricity consumption prediction: Incorporating machine learning models and optimization algorithms, Energy, 296, 131259. https://doi.org/10.1016/j.energy.2024.131259
Liu, H. & Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Appl. Sci., 9, 4396.
Meydani, A., Shahinzadeh, H., Ramezani, A., Moazzami, M., Nafisi H. & Askarian-Abyaneh, H. (2024). Comprehensive review of artificial intelligence applications in smart grid operations. In 9th International Conference on Technology and Energy Management (ICTEM), Behshar, Mazandaran, Iran, 1-13. doi: 10.1109/ICTEM6069 0.2024.10631952
Muralitharan, K., Sakthivel, R. & Vishnuvarthan, R. (2018). Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing, 273, 199–208.
Narendran, S. & Yakkala, B. (2024). Advancing smart grid stability through machine learning and principal component analysis. 13th International Conference on System Modeling & Advancement in Research Trends (SMART), IEEE, 814-819.
Onder, M., Dogan, M. U. & Polat, K. (2023). Classification of smart grid stability prediction using cascade machine learning methods and the internet of things in smart grid. Neural Computing and Applications, 35(24), 17851-17869.
Rastogi, A. K., Taterh, S. & Kumar, B. S. (2023). Dimensionality reduction algorithms in machine learning: A theoretical and experimental comparison. Engineering Proceedings, 59(1), 82.
Schäfer, B., Grabow, C., Auer, S., Kurths, J., Witthaut, D. & Timme, M. (2016). Taming instabilities in power grid networks by decentralized control. Eur. Phys. J. Spec. Top., 225, 569–582. https://doi.org/10.1140/epjst/e2015-50136-y
Shi, Z., Yao, W., Li, Z., Zeng, L., Zhao, Y., Zhang, R., Tang, Y. & Wen, J. (2020). Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions. Applied Energy, 278, 115733.
Taherdoost, H. (2024). A systematic review of big data innovations in smart grids. Results in Engineering, 22, 102132.
Tiwari, S., Jain, A., Ahmed, N.S., Charu, Alkwai, L.M., Dafhalla, A.K.Y. & Hamad, S.A.S. (2022). Machine learning‐based model for prediction of power consumption in smart grid‐ smart way towards smart city. Expert Syst., 39, e12832. https://doi.org/10.1111/exsy.12832
Tufail, S., Iqbal, H., Tariq, M. & Sarwat, A. (2025). A hybrid machine learning-based framework for data injection attack detection in smart grids using pca and stacked autoencoders. IEEE Access, 13. doi:10.1109/ACCESS.2025.3543751
Waheed, W. & Xu, Q. (2024). Data-driven short term load forecasting with deep neural networks: Unlocking insights for sustainable energy management. Electric Power Systems Research, 232, 110376. https://doi.org/10.1016/j.epsr.2024.110376
Wang, Q., Yao, W., Fang, J., Ai, X., Wen, J., Yang, X., Xie, H. & Huang, X. (2020). Dynamic modeling and small signal stability analysis of distributed photovoltaic grid-connected system with large scale of panel level dc optimizers. Appl. Energy, 259, 114132.
You, S., Zhao, Y., Mandich, M., Cui, Y., Li, H., Xiao, H., Fabus, S., Su, Y., Liu, Y. & Yuan, H. (2020). A review on artificial intelligence for grid stability assessment. In Proceedings of the 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Tempe, AZ, USA, 11–13 November 2020, pp. 1–6.
Zahid, M., Ahmed, F., Javaid, N., Abbasi, R.A., Zainab Kazmi, H.S., Javaid, A., Bilal, M., Akbar, M. & Ilahi, M. (2019). Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics, 8, 122.
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How to Cite
Machine learning based smart grid prediction models for enhancing environmental sustainability. (2026). Journal of Applied and Natural Science, 18(2), 626-640. https://doi.org/10.31018/jans.v18i2.7443
