ELECTRIFAI: An AI-Driven Solution for Enhancing Electricity Distribution Efficiency and Preventing Unauthorized Power Tapping in the Philippines

The Philippines faces significant challenges in its power sector, including power leakage, losses, and unauthorized electricity tapping. These issues led to substantial financial and environmental impacts, particularly for utility companies and local power cooperatives. ElectrifAI aims to enhance the efficiency of electricity distribution and mitigate losses through advanced technological solutions. The application integrates sensors and real-time data analysis to detect and address power leakages and inefficiencies. It utilizes the Power Usage Effectiveness (PUE) metric to measure and improve energy efficiency. ElectrifAI also combats unauthorized electricity tapping by employing advanced algorithms like the Isolation Forest, achieving a 98% accuracy rate in detecting anomalies. The system's smart grid capabilities allow proactive maintenance and immediate issue resolution, reducing operational costs and ensuring a stable power supply. Furthermore, a mobile application provides consumers with real-time insights into their energy consumption, promoting energy-saving behaviors. The implementation process involves meticulous component selection, integration of IoT devices, and extensive testing. Based on field trials, it demonstrated ElectrifAI's effectiveness in detecting unauthorized tapping and analyzing real-time data. By modernizing the power distribution network and integration of AI-driven solutions, ElectrifAI significantly enhances the reliability, efficiency, and sustainability of the power sector in the Philippines.

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Publication Details

Published
2024-12-01
DOI
https://doi.org/10.1109/tencon61640.2024.10903080
Primary Topic
Electricity Theft Detection Techniques
Type
article
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article

ELECTRIFAI: An AI-Driven Solution for Enhancing Electricity Distribution Efficiency and Preventing Unauthorized Power Tapping in the Philippines

Gerhard P. Tan, Raeselyn C. Macorol, Daniel S. Caparro, Justine Rey Y. Daquis et al.
Electricity Theft Detection Techniques
article

ELECTRIFAI: An AI-Driven Solution for Enhancing Electricity Distribution Efficiency and Preventing Unauthorized Power Tapping in the Philippines

Gerhard P. Tan, Raeselyn C. Macorol, Daniel S. Caparro, Justine Rey Y. Daquis, Clarence Jay M. Fetalino, Mcallen G. Fradejas
article en

Abstract

The Philippines faces significant challenges in its power sector, including power leakage, losses, and unauthorized electricity tapping. These issues led to substantial financial and environmental impacts, particularly for utility companies and local power cooperatives. ElectrifAI aims to enhance the efficiency of electricity distribution and mitigate losses through advanced technological solutions. The application integrates sensors and real-time data analysis to detect and address power leakages and inefficiencies. It utilizes the Power Usage Effectiveness (PUE) metric to measure and improve energy efficiency. ElectrifAI also combats unauthorized electricity tapping by employing advanced algorithms like the Isolation Forest, achieving a 98% accuracy rate in detecting anomalies. The system's smart grid capabilities allow proactive maintenance and immediate issue resolution, reducing operational costs and ensuring a stable power supply. Furthermore, a mobile application provides consumers with real-time insights into their energy consumption, promoting energy-saving behaviors. The implementation process involves meticulous component selection, integration of IoT devices, and extensive testing. Based on field trials, it demonstrated ElectrifAI's effectiveness in detecting unauthorized tapping and analyzing real-time data. By modernizing the power distribution network and integration of AI-driven solutions, ElectrifAI significantly enhances the reliability, efficiency, and sustainability of the power sector in the Philippines.

Polytechnic University of the Philippines (PH)
Affordable and clean energy
Openalex Percentile: Top 74%
Electricity Theft Detection Techniques
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