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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM59757

AI-POWERED SMART DISTRIBUTION BOARD

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM59757

Volume:

10 |

4 |

April 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM59757

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DGJSD

Dr. G. Justin Sunil Dhas

EEE

Vimal Jyothi Engineering College, Chemperi, Kannur, India

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PLJ

Prof. Laly James

EEE

Vimal Jyothi Engineering College, Chemperi, Kannur, India

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APP

Aromal P P

EEE

Vimal Jyothi Engineering College, Chemperi, Kannur, India

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VK

Vidhu Krishnan

EEE

Vimal Jyothi Engineering College, Chemperi, Kannur, India

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GK

Gautham Krishnan

EEE

Vimal Jyothi Engineering College, Chemperi, Kannur, India

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NR

Neeraj Ranjith

EEE

Vimal Jyothi Engineering College, Chemperi, Kannur, India

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Abstract

The AI-Powered Smart Distribution Board is designed to address critical inefficiencies in conventional electrical systems, such as load imbalance, low power factor, and the absence of real-time monitoring. By integrating artificial intelligence and automation, the system provides advanced energy management features. The primary functions include predicting electrical load demand using AI-based forecasting, which is crucial for optimized energy management. It automatically balances phase loads through intelligent switching mechanisms using relays or contactors. Power quality is improved, and energy costs are reduced by integrating capacitor banks for automated power factor correction. Furthermore, the system incorporates an IoT-enabled, real-time dashboard that facilitates remote monitoring, control, and fault alerts, significantly enhancing operational reliability and efficiency. This innovative solution is scalable, promotes sustainable energy practices, and is ideal for modern residential and commercial applications.

KEYWORDS

AI Smart Distribution Board Load Forecasting Power Factor Correction IoT ESP32 Load Balancing Energy Management Automation

Cite This Article

Dr. G. Justin Sunil Dhas, Prof. Laly James, Aromal P P, Vidhu Krishnan, Gautham Krishnan, Neeraj Ranjith, "AI-POWERED SMART DISTRIBUTION BOARD," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-4, April 2026. https://doi.org/10.55041/IJSREM59757

Full Article/Conclusion

The AI-Powered Smart Distribution Board successfully proposes a comprehensive solution to overcome the limitations of conventional electrical distribution systems. By leveraging Artificial Intelligence and IoT, the system provides automatic load balancing, dynamic power factor correction, and real-time remote monitoring. These objectives ensure enhanced system efficiency, greater reliability, and substantial energy cost savings, positioning it as a viable and sustainable energy management solution for the future. The implementation of automated control mechanisms including capacitor banks and relay-based load switching enables timely corrective actions. The integration of cloud connectivity and mobile/web dashboard allows operators to monitor remotely and receive instant alerts, thereby improving operational efficiency and reducing manual effort. Overall, the proposed system provides a cost-effective, scalable, and user-friendly approach to electrical distribution management. It enhances productivity, optimizes resource utilization, and supports sustainable energy practices. By leveraging IoT and smart AI technologies, this system offers a practical solution advancing intelligent energy infrastructure.  

References

1.   Y.-H. Lin, H.-S. Tang, T.-Y. Shen and C.-H. Hsia, "A Smart Home Energy Management System Utilizing Neurocomputing-Based Time-Series Load Modeling and Forecasting," in IEEE Access, vol. 10, pp. 116747-116765, 2022, doi: 10.1109/ACCESS.2022.3219068. 2.   M. U. Saleem, M. R. Usman, M. A. Yaqub, A. Liotta and A. Asim, "Smarter Grid in the 5G Era: Integrating the Internet of Things With a Cyber-Physical System," in IEEE Access, vol. 12, pp. 34002-34018, 2024, doi: 10.1109/ACCESS.2024.3372379. 3.   M. U. Saleem, M. R. Usman, M. A. Usman and C. Politis, "Design, Deployment and Performance Evaluation of an IoT Based Smart Energy Management System for Demand Side Management in Smart Grid," in IEEE Access, vol. 10, pp. 15261-15278, 2022, doi: 10.1109/ACCESS.2022.3147484. 4.   C. L. Athanasiadis, T. A. Papadopoulos, G. C. Kryonidis and D. I. Doukas, "A Holistic and Personalized Home Energy Management System With Non-Intrusive Load Monitoring," in IEEE Transactions on Consumer Electronics, vol. 70, no. 4, pp. 6725-6737, Nov. 2024, doi: 10.1109/TCE.2024.3483938. 5.    J. Lina, "Design of Smart Home Energy Efficiency Management System Based on BLE Technology," 2022 4th Int. Conf. on Machine Learning, Big Data and Business Intelligence (MLBDBI), Shanghai, China, 2022, pp. 201-206, doi: 10.1109/MLBDBI58171.2022.00046. 6.   A. S. B. Aidell, N. b. Junaidi, K. b. Kipli, S. M. bt Wan Masra and A.    b. Lit, "IoT-Enabled Demand Response and Energy Management System for Smart Homes," 2024 IEEE Sustainable Power and Energy Conference (iSPEC), Kuching, Malaysia, 2024, pp. 479-483, doi: 10.1109/iSPEC59716.2024.10892582. 7.    Y. Ge, W. Zhang, G. Liu, Z. Li and H. Li, "Adaptive Feature Selection for Probabilistic Multi-Energy Load Forecasting," in IEEE Transactions on Industry Applications, vol. 61, no. 1, pp. 1341-1351, Jan.-Feb. 2025, doi: 10.1109/TIA.2023.3344540. 8.    B. Krishnaveni, S. Saravanan, B. Sathwik, B. Pragna and V. A, "IoT-Enabled Power Factor Monitoring and Correction with PZEM Integration," 2024 IEEE Third International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, 2024, pp. 885-890, doi: 10.1109/ICPEICES62430.2024.10719125. 9.   V. Kumar, R. K. Mandal and S. Rai, "Smart metered distribution system load forecasting using statistical approaches," 2024 4th International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), Patna, India, 2024, pp. 1-4, doi: 10.1109/ICEFEET64463.2024.10866475. 10.   Ozdemir, Y. Demir and O. Yildirim, "The Effect of Input Length on Prediction Accuracy in Short-Term Multi-Step Electricity Load Forecasting: A CNN-LSTM Approach," in IEEE Access, vol. 13, pp. 28419-28432, 2025, doi: 10.1109/ACCESS.2025.3540636.

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Publication Date

7/15/2026

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Review Rounds: 2

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