Deep Learning Fraud Detection in solar Panel Systems to Enhance Reliability and Performance.
Abstract & Details
Research Area
Computer Engineering
Keywords
Deep Learning
Fraud detection
Solar panel systems
Renewable energy
Energy meter tampering
Data hacking
Long Short-Term Memory (LSTM)
Anomaly detection
System reliability
Performance optimisation
Real-time sensor data. Energy production patterns
Billing fraud
Smart energy systems
Weather impact on energy
Abstract
Solar panels elicit such powerful renewable energy that all of them have been digitalized for fraudulent activity as their demand is proliferated. Energy meter tampering, data hacking are fraud activities in solar systems which reduce the reliability and performance. In this paper, a deep learning architecture is proposed to identify fraudulent activities in the solar panel system based on historical performance data, real-time sensor inputs and the weather. Energy production and billing patterns are detected using a Long Short-Term Memory (LSTM) network. Our experiments show that the model significantly increases fraud detection accuracy and improves system reliability and operational performance.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Chandan Kumar Choudary | CMR University |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Choudary, Chandan Kumar (2024). Deep Learning Fraud Detection in solar Panel Systems to Enhance Reliability and Performance.. International Journal of Advance Research and Innovative Ideas In Education, 10(5), 664-670.
MLA Style
Choudary, Chandan Kumar. "Deep Learning Fraud Detection in solar Panel Systems to Enhance Reliability and Performance.." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 5, 2024, pp. 664-670.
IEEE Style
Chandan Kumar Choudary, "Deep Learning Fraud Detection in solar Panel Systems to Enhance Reliability and Performance.," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 5, pp. 664-670, 2024.
Vancouver Style
Choudary Chandan Kumar. Deep Learning Fraud Detection in solar Panel Systems to Enhance Reliability and Performance.. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(5):664-670.
Harvard Style
Choudary, Chandan Kumar (2024) 'Deep Learning Fraud Detection in solar Panel Systems to Enhance Reliability and Performance.', International Journal of Advance Research and Innovative Ideas In Education, 10(5), pp. 664-670.
Chicago Style
Choudary, Chandan Kumar. "Deep Learning Fraud Detection in solar Panel Systems to Enhance Reliability and Performance.." International Journal of Advance Research and Innovative Ideas In Education 10, no. 5 (2024): 664-670.
Turabian Style
Choudary, Chandan Kumar. "Deep Learning Fraud Detection in solar Panel Systems to Enhance Reliability and Performance.." International Journal of Advance Research and Innovative Ideas In Education 10, no. 5 (2024): 664-670.
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