Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing

Abdulkadir Gozuoglu; Muhammed Elhattab

doi:10.17798/bitlisfen.1748636

Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing

Abstract

This study uses a multi-tier edge computing architecture to present a lightweight, real-time anomaly detection and Rule-Based Causal reasoning framework for energy systems. The system combines ultra-low-power microcontrollers (ESP32/STM32) at Tier 1 for sensing and on-device TinyML-based anomaly inference, a local RISC-V-based embedded host (Lichee RV Dock) at Tier 2 for Rule-Based Causal analysis and dashboard visualization, and optional cloud platforms (Firebase, ThingsBoard, AWS IoT) at Tier 3 for extended services. Real-time voltage, current, and temperature data are collected, processed locally, and interpreted using rule-based Causal logic. Experimental results demonstrate the system’s low-latency performance (~0.2s), high anomaly detection precision (95.2%), and effective interpretability in edge deployments. The proposed TinyML-based model achieved a validation accuracy of 94.8% and a precision of 95.2%, demonstrating robust performance in anomaly classification under real-time edge-deployment conditions.

Keywords

Ethical Statement

The study is complied with research and publication ethics.

Thanks

The authors declare that no specific funding, institutional support, or external assistance was received for this study. No ethical approval was required for this research.

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Details

Primary Language

English

Subjects

Electrical Energy Transmission, Networks and Systems

Journal Section

Research Article

Authors

Abdulkadir Gozuoglu ^*
0000-0002-6968-379X
Türkiye

Muhammed Elhattab This is me
Türkiye

Publication Date

June 30, 2026

Submission Date

July 22, 2025

Acceptance Date

April 22, 2026

Published in Issue

Year 2026 Volume: 15 Number: 2

DOI

https://doi.org/10.17798/bitlisfen.1748636

IZ

https://izlik.org/JA63JS36XA

Cite

RIS / Bibtex

APA

Gozuoglu, A., & Elhattab, M. (2026). Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 15(2), 515-529. https://doi.org/10.17798/bitlisfen.1748636

AMA

1.Gozuoglu A, Elhattab M. Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15(2):515-529. doi:10.17798/bitlisfen.1748636

Chicago

Gozuoglu, Abdulkadir, and Muhammed Elhattab. 2026. “Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 (2): 515-29. https://doi.org/10.17798/bitlisfen.1748636.

EndNote

Gozuoglu A, Elhattab M (June 1, 2026) Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 2 515–529.

IEEE

[1]A. Gozuoglu and M. Elhattab, “Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 2, pp. 515–529, June 2026, doi: 10.17798/bitlisfen.1748636.

ISNAD

Gozuoglu, Abdulkadir - Elhattab, Muhammed. “Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15/2 (June 1, 2026): 515-529. https://doi.org/10.17798/bitlisfen.1748636.

JAMA

1.Gozuoglu A, Elhattab M. Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15:515–529.

MLA

Gozuoglu, Abdulkadir, and Muhammed Elhattab. “Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 2, June 2026, pp. 515-29, doi:10.17798/bitlisfen.1748636.

Vancouver

1.Abdulkadir Gozuoglu, Muhammed Elhattab. Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026 Jun. 1;15(2):515-29. doi:10.17798/bitlisfen.1748636