Efficient Usage of Energy Infrastructure in Smart City Using Machine Learning


  • Rajesh Rajaan Swami Keshvanand Institute of Technology, Management and Gramothan
  • Bhaskar Kamal Baishya Golaghat
  • Tulasi Vigneswara Rao Nicmar University
  • Balachandra Pattanaik Wallaga Univeristy
  • Mano Ashish Tripathi Motilal Nehru National Institute of Technology image/svg+xml
  • Anitha R R M Valliamai Engineering College




Smart cities, Energy consumption, Cost efficient, Machine Learning


The concept of smart cities revolves around utilizing modern technologies to manage and optimize city operations, including energy infrastructure. One of the biggest problems that smart cities have to deal with is ensuring the efficient usage of energy infrastructure to reduce energy consumption, cost, and environmental impact. Machine learning is a powerful tool that can be utilized to optimize energy usage in smart cities. This paper proposes a framework for efficient usage of energy machine learning for city infrastructure in smart cities. The proposed framework includes three main components: data collection, machine learning model development, and energy infrastructure optimization. The data collection component involves collecting energy consumption data from various sources, such as smart meters, sensors, and other IoT devices. The collected data is then pre-processed and cleaned to remove any inconsistencies or errors. The machine learning model development component involves developing machine learning models to predict energy consumption and optimize energy usage. The models can be developed using various techniques such as regression, classification, clustering, and deep learning. These models can predict energy consumption patterns based on historical data, weather conditions, time of day, and other factors. The energy infrastructure optimization component involves utilizing the machine learning models to optimize energy usage. The optimization process involves adjusting energy supply and demand to reduce energy consumption and cost. The optimization process can be automated, and SVM based machine learning models can continuously enhance their precision over time by studying the data. The proposed framework has several benefits, including reducing energy consumption, cost, and environmental impact. It can also improve the reliability and stability of energy infrastructure, reduce the risk of blackouts, and improve the overall quality of life in highly developed urban areas. Last but not least, the projected framework for efficient usage of energy machine learning for city infrastructure in smart cities is a promising solution to optimize energy usage and reduce energy consumption and cost. The framework can be implemented in various smart city applications, including buildings, transportation, and industrial processes.


Download data is not yet available.
<br data-mce-bogus="1"> <br data-mce-bogus="1">


Yoon, Guwon et al. “Prediction of Machine Learning Base for Efficient Use of Energy Infrastructure in Smart City.” 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE) (2019): 32-35. DOI: https://doi.org/10.1109/iCCECE46942.2019.8941864

Demir, Idris et al. “The Core of a Smart Grid: Internet of Energy and Machine Learning.” 2022 Global Energy Conference (GEC) (2022): 357-360. DOI: https://doi.org/10.1109/GEC55014.2022.9987139

Tang, Ziqiang et al. “Machine Learning Assisted Energy Optimization in Smart Grid for Smart City Applications.” J. Interconnect. Networks 22 (2022): 2144006:1-2144006:23. DOI: https://doi.org/10.1142/S0219265921440060

Shahriar, Sakib et al. “Prediction of EV Charging Behavior Using Machine Learning.” IEEE Access 9 (2021): 111576-111586. DOI: https://doi.org/10.1109/ACCESS.2021.3103119

Srihith, I. Venkata Dwaraka et al. “Future of Smart Cities: The Role of Machine Learning and Artificial Intelligence.” South Asian Research Journal of Engineering and Technology (2022): n. pag. DOI: https://doi.org/10.36346/sarjet.2022.v04i05.005

Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6

Rahat IS, Ghosh H, Shaik K, Khasim S, Rajaram G. Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 29 [cited 2023 Oct. 2];9.https://doi.org/10.4108/eetpht.9.4016 DOI: https://doi.org/10.4108/eetpht.9.4016

Ghosh H, Rahat IS, Shaik K, Khasim S, Yesubabu M. Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks. EAI Endorsed Scal Inf Syst [Internet]. 2023 Sep. 21https://doi.org/10.4108/eetsis.3937 DOI: https://doi.org/10.4108/eetsis.3937

Mandava, S. R. Vinta, H. Ghosh, and I. S. Rahat, “An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population”, EAI Endorsed Trans Perv Health Tech, vol. 9, Oct. 2023.https://doi.org/10.4108/eetpht.9.4052 DOI: https://doi.org/10.4108/eetpht.9.4052

Mandava, M.; Vinta, S. R.; Ghosh, H.; Rahat, I. S. Identification and Categorization of Yellow Rust Infection in Wheat through Deep Learning Techniques. EAI Endorsed Trans IoT 2023, 10. https://doi.org/10.4108/eetiot.4603 DOI: https://doi.org/10.4108/eetiot.4603

Khasim, I. S. Rahat, H. Ghosh, K. Shaik, and S. K. Panda, “Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023 https://doi.org/10.4108/eetiot.4579 DOI: https://doi.org/10.4108/eetiot.4579

Khasim, H. Ghosh, I. S. Rahat, K. Shaik, and M. Yesubabu, “Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements”, EAI Endorsed Trans IoT, vol. 10, Nov. 2023. https://doi.org/10.4108/eetiot.4484 DOI: https://doi.org/10.4108/eetiot.4484

Mohanty, S.N.; Ghosh, H.; Rahat, I.S.; Reddy, C.V.R. Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh. Eng. Proc. 2023, 59, 69.https://doi.org/10.3390/engproc2023059069 DOI: https://doi.org/10.3390/engproc2023059069

Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470 DOI: https://doi.org/10.3390/w13233470

Babu, SBG Tilak, V. Satyanarayana, and Ch Srinivasarao. "Shift invarient and Eigen feature based image fusion." International Journal on Cybernetics & Informatics (IJCI) 5.4 (2016). DOI: https://doi.org/10.5121/ijci.2016.5418

“Climate Emergency Declaration and Mobilisation In Action,” CEDAMIA. https://www.cedamia.org/global/ (accessed Jan. 17, 2021).

“Key World Energy Statistics 2018 – Analysis,” IEA. https://www.iea.org/reports/key-world-energy-statistics-2019 (accessed Jun. 01, 2020). “68% of the world population projected to live in urban areas by 2050, says UN,” UN DESA | United Nations Department of Economic and Social Affairs, May 16, 2018. https://www.un.org/development/desa/en/news/population/2018-revision of-world-urbanization-prospects.html (accessed Jun. 01, 2020).

X. Zhang, F. Gao, X. Gong, Z. Wang, and Y. Liu, “Comparison of Climate Change Impact Between Power System of Electric Vehicles and Internal Combustion Engine Vehicles,” in Advances in Energy and Environmental Materials, Singapore, 2018, pp. 739–747. DOI: https://doi.org/10.1007/978-981-13-0158-2_75

11.“Global EV Outlook 2019 – Analysis,” IEA. https://www.iea.org/reports/global-ev-outlook-2019 (accessed Jun. 01, 2020).

R. Lal, “Home gardening and urban agriculture for advancing food and nutritional security in response to the COVID-19 pandemic,” Food Secur., vol. 12, no. 4, pp. 871–876, 2020. DOI: https://doi.org/10.1007/s12571-020-01058-3

G. Lăzăroiu, L. Ionescu, C. Uță, I. Hurloiu, M. Andronie, and I. Dijmărescu, “Environmentally responsible behavior and sustainability policy adoption in green public procurement,” Sustainability, vol. 12, no. 5, p. 2110, 2020. DOI: https://doi.org/10.3390/su12052110

R. Bucea-Manea-Țoniş et al., “Blockchain Technology Enhances Sustainable Higher Education,” Sustainability, vol. 13, no. 22, p. 12347, 2021. DOI: https://doi.org/10.3390/su132212347

A. S. Anwar, U. Rahardja, A. G. Prawiyogi, N. P. L. Santoso, and S. Maulana, “iLearning Model Approach in Creating Blockchain Based Higher Education Trust,” Int. J. Artif. Intell. Res, vol. 6, no. 1, 2022. DOI: https://doi.org/10.29099/ijair.v6i1.258

M. B. Péron, “Optimal sequential decision-making under uncertainty.” Queensland University of Technology, 2018.

M. Hyland and H. S. Mahmassani, “Operational benefits and challenges of shared-ride automated mobility-on-demand services,” Transp. Res. Part A Policy Pract., vol. 134, pp. 251– 270, 2020. DOI: https://doi.org/10.1016/j.tra.2020.02.017

T. Lykouris, M. Simchowitz, A. Slivkins, and W. Sun, “Corruption-robust exploration in episodic reinforcement learning,” in Conference on Learning Theory, 2021, pp. 3242–3245.

A. Agnesina, K. Chang, and S. K. Lim, “VLSI placement parameter optimization using deep reinforcement learning,” in Proceedings of the 39th International Conference on Computer-Aided Design, 2020, pp. 1–9. DOI: https://doi.org/10.1145/3400302.3415690

S. Kapoor, “Multi-agent reinforcement learning: A report on challenges and approaches,” arXiv Prepr. arXiv1807.09427, 2018. [22] F. Bu and X. Wang, “A smart agriculture IoT system based on deep reinforcement learning,” Futur. Gener. Comput. Syst., vol. 99, pp. 500–507, 2019. DOI: https://doi.org/10.1016/j.future.2019.04.041




How to Cite

R. Rajaan, B. K. Baishya, T. V. Rao, B. Pattanaik, M. A. Tripathi, and A. R, “Efficient Usage of Energy Infrastructure in Smart City Using Machine Learning”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.

Most read articles by the same author(s)