A Secure IIoT Environment That Integrates AI-Driven Real-Time Short-Term Active and Reactive Load Forecasting with Anomaly Detection: A Real-World Application DOI Creative Commons
Md. Ibne Joha,

Md Minhazur Rahman,

Md Shahriar Nazim

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(23), P. 7440 - 7440

Published: Nov. 21, 2024

The Industrial Internet of Things (IIoT) revolutionizes both industrial and residential operations by integrating AI (artificial intelligence)-driven analytics with real-time monitoring, optimizing energy usage, significantly enhancing efficiency. This study proposes a secure IIoT framework that simultaneously predicts active reactive loads while also incorporating anomaly detection. system is optimized for deployment on an edge server, such as single-board computer (SBC), well cloud or centralized server. It ensures reliable smart data acquisition systems control, protective measures. We propose Temporal Convolutional Networks-Gated Recurrent Unit-Attention (TCN-GRU-Attention) model to predict loads, which demonstrates superior performance compared other conventional models. metrics load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Absolute (MAE), 0.1354 Root (RMSE), forecasting, the 0.0202 0.1077 0.1422 (RMSE). Furthermore, we introduce Isolation Forest detection considers transient conditions appliances when identifying irregular behavior. very promising performance, average all using this being 95% Precision, 98% Recall, 96% F1 Score, nearly 100% Accuracy. To entire system, Transport Layer Security (TLS) Secure Sockets (SSL) security protocols employed, along hash-encoded encrypted credentials enhanced protection.

Language: Английский

Implementation a home load power management system based on a wireless sensor network DOI

Saja Mohsin Abood,

Kasim K. Abdulla,

Shamam Alwash

et al.

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3282, P. 030022 - 030022

Published: Jan. 1, 2025

Language: Английский

Citations

0
Transforming the electrical grid: the role of AI in advancing smart, sustainable, and secure energy systems DOI Creative Commons

T. A. Rajaperumal,

C. Christopher Columbus

Energy Informatics, Journal Year: 2025, Volume and Issue: 8(1)

Published: April 16, 2025

Language: Английский

Citations

0
A Secure IIoT Environment That Integrates AI-Driven Real-Time Short-Term Active and Reactive Load Forecasting with Anomaly Detection: A Real-World Application DOI Creative Commons
Md. Ibne Joha,

Md Minhazur Rahman,

Md Shahriar Nazim

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(23), P. 7440 - 7440

Published: Nov. 21, 2024

The Industrial Internet of Things (IIoT) revolutionizes both industrial and residential operations by integrating AI (artificial intelligence)-driven analytics with real-time monitoring, optimizing energy usage, significantly enhancing efficiency. This study proposes a secure IIoT framework that simultaneously predicts active reactive loads while also incorporating anomaly detection. system is optimized for deployment on an edge server, such as single-board computer (SBC), well cloud or centralized server. It ensures reliable smart data acquisition systems control, protective measures. We propose Temporal Convolutional Networks-Gated Recurrent Unit-Attention (TCN-GRU-Attention) model to predict loads, which demonstrates superior performance compared other conventional models. metrics load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Absolute (MAE), 0.1354 Root (RMSE), forecasting, the 0.0202 0.1077 0.1422 (RMSE). Furthermore, we introduce Isolation Forest detection considers transient conditions appliances when identifying irregular behavior. very promising performance, average all using this being 95% Precision, 98% Recall, 96% F1 Score, nearly 100% Accuracy. To entire system, Transport Layer Security (TLS) Secure Sockets (SSL) security protocols employed, along hash-encoded encrypted credentials enhanced protection.

Language: Английский

Citations

0