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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63894
Volume:
10 |
5 |
May 2026
Pages:
1-4
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.
Master Of Computer Application
Bapuji Institute Of Engineering And Technology, Davangere
Master Of Computer Application
Bapuji Institute Of Engineering And Technology, Davangere
The rapid deployment of smart meters and IoT devices in modern power systems has led to an exponential growth of energy consumption data. Traditional statistical forecasting techniques struggle to manage the volume, velocity, and variety of such data. This research proposes a Big Data-driven predictive analytics framework for Smart Grids using Hadoop, Apache Spark, and Deep Learning models such as Long Short-Term Memory (LSTM). The system supports real-time anomaly detection, scalable distributed processing, and improved forecasting accuracy, enabling proactive grid management and operational efficiency.
KEYWORDS
This project successfully demonstrates the transformation of traditional energy forecasting into a Big Data-driven intelligent system. By integrating distributed storage, parallel processing, and deep learning models, the proposed system provides: · Accurate energy load forecasting · Real-time anomaly detection · Scalable infrastructure · Optimized grid management The system contributes toward building autonomous and self-healing Smart Grids.
1. Al-Fuqaha A. Guizani M. Mohammadi M. Aledhari M.& Ayyash M. (2015). Internet of Things: A Survey on Enabling Technologies Protocols and Applications. IEEE Communications Surveys & Tutorials 17(4) 2347-2376. 2. Hochreiter S. & Schmidhuber J. (1997). Long Short-Term Memory. Neural Computation 9(8) 1735 1780. (Foundational paper for the LSTM algorithm used). 3. Chen M. Mao S. & Liu Y. (2014). Big Data: A Survey. Mobile Networks and Applications 19(2) 171-209. 4. ang X. Misra S. Xue G. & Yang D. (2012). Smart Grid — The New and Improved Power Grid: A Survey. IEEE Communications Surveys & Tutorials 14(4) 944-980. 5. White T. (2015). Hadoop: The Definitive Guide. 4th Edition. O'Reilly Media. (Reference for the Big Data architecture implementation).
Publication Date
5/24/2026
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.
Funding Information
No funding information available for this research.
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