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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63816
Volume:
10 |
5 |
May 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
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.
Masters of computer application
bapuji institute of engineering and technology davangeri
Masters of computer application
bapuji institute of engineering and technology davangeri
One of the most crucial elements of the modern power infrastructures effective operation is the ability to forecast electricity demand. Demand forecasting is complicated because electricity consumption varies greatly depending on a number of factors including environmental patterns time of day seasonal variations and consumer habits. This reports suggested project is the creation of an AI-based system that provides the peak load and electricity demand based on time-related meteorological and historical data. The proposed project seeks to improve the accuracy of electricity peak load forecasting. Machine learning and time-series models are used when electricity demand is at its highest. Customers are able to provide instant results of the forecasts of electricity utilization and necessary costs thanks to this suggested system.
KEYWORDS
The suggested Electricity Demand and Peak Load Forecasting System effectively forecasts future periods of peak load and electricity demand by utilizing AI-based models historical data and meteorological variables. Utilities can enhance planning avoid overloads and preserve grid stability by using the processes of data preprocessing feature selection model training and evaluation to ensure accurate and dependable forecasting. While performance testing verifies the efficacy and efficiency of the system in smart energy management graphical user interfaces improve usability. In order to make the system more scalable flexible and appropriate for contemporary smart grid infrastructure future enhancements will incorporate real-time and renewable energy data add predictive cost and fault analysis enable multi-regional forecasting and implement cloud-based deployment with automated model retraining.
1. H. L. Willis and W. G. Scott Distributed Power Generation: Planning and Evaluation CRC Press 2016. 2. A. Khotanzad R. Afkhami-Rohani and H. Zareipour “A Neuro-Fuzzy Approach to Short- Term Load Forecasting in Power Systems” IEEE Transactions on Power Systems vol. 3. Box Jenkins and Reinsel Time Series Analysis: Forecasting and Control Wiley 2015 4. Y. Chen P. Wang and L. Wu “Short-Term Electricity Load Forecasting Using Deep Learning Based LSTM Networks” 5. A. Smyl “A Hybrid Method of Exponential Smoothing and Recurrent Neural Networks for Time Series Forecasting” International Journal of Forecasting vol. 36 no. 1 pp. 75-85 2020 6. C. Zhang Y. Zhang and J. Wang "Electric Load Forecasting Using a Deep Neural Network" Energy vol. 198 pp. 117197 7. M. Hong J. Kim and S. Lee "Peak Load Forecasting for Smart Grids Based on Machine Learning Techniques "Applied Energy vol. 285 pp. 116378 2021.G. Gross and F. D. Galiana “Short-Term Load Forecasting” Proceedings of the IEEE vol. 75 no. 12 pp. 1558–1573 1987.
Publication Date
5/23/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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