Skip to main content
Kaggle

IoT based Industrial Power Generation and Distribution Monitoring Dataset

Industrial IoT & Predictive Maintenance Industrial IoT
279 views
1 min read
License
Catalog metadata: This page is a discovery record, not publisher documentation. Verify the description, schema, provenance, version, licence, and citation at the linked source before use.

Catalog Summary

"Industrial IoT dataset for efficient monitoring and control of power generation and distribution processes in smart grid applications with real-time fault detection capabilities."

Catalog Notes

Comprehensive industrial IoT dataset designed for efficient monitoring and control of power generation and distribution processes in smart grid environments. Dataset enables integration of distributed power generation sources into transmission and distribution systems under realistic network scenarios.

Supports reliable and dynamic data capacity requirements for advanced cyber-physical systems equipped with sensors and devices for optimal operation through manual or automatic controls. Provides real-time operational information to utilities for enhanced grid management.

Features intelligent monitoring scheme that identifies faulty systems in remote locations and notifies users in real-time, enabling appropriate actions to maintain steady electricity supply to customers. Developed by researchers from Universiti Teknologi Malaysia and Abdullah Gul University.

View Data Structure

To explore column names, data types, and sample rows, visit the official dataset page on Kaggle.

Preview on Kaggle

Cite This Dataset

Faheem, M., Fizza, G., Waqar, M. W., Aslam Butt, R., Ngadi, M. A., & Gungor, V. C. (2021). Dataset acquired by Internet of Things-enabled Industrial Multichannel Wireless Sensors Networks for Active Monitoring and Control in the Smart Grid Industry 4.0.. [Dataset]. Mendeley Data. https://doi.org/10.17632/32d6r6r6zk.1

Source metadata: Mendeley Data (2021) · DOI: 10.17632/32d6r6r6zk.1

Indexed by IoTDataset.com on Feb 08, 2026

Review the Source Record

Confirm the licence, version, access conditions, file format, and provenance at the source before use.

Open Source Page

Related Topics & Keywords

Browse all Industrial IoT & Predictive Maintenance datasets

Share This Research

More in Industrial IoT & Predictive Maintenance

View All
Industrial IoT Zenodo

IEC 60870-5-104 Intrusion Detection Dataset — Smart Grid Cyberattacks [1.1 GB]

Smart-grid IDS dataset with labelled IEC 60870-5-104 and TCP/IP flow statistics plus PCAP files across 12 cyberattack scenarios. CSV and PCAP formats.

Jun 02, 2026
Industrial IoT Zenodo

DNP3 Intrusion Detection Dataset — Industrial SCADA Cyberattacks [194.9 MB]

Industrial IoT IDS dataset with labelled TCP/IP and DNP3 flow statistics plus PCAP files for 9 SCADA cyberattacks. CSV and PCAP formats for ML/DL IDS research.

Jun 02, 2026
Industrial IoT Kaggle

IoT-Integrated Predictive Maintenance Dataset

Time-series sensor readings from industrial machines for predictive maintenance and anomaly detection applications.

Feb 19, 2026
Industrial IoT Kaggle

Post-Quantum Cryptography Impact in Industrial IoT

Released in October 2025, this dataset captures performance metrics and network traffic associated with implementing Post-Quantum Cryptography (PQC) in Industrial IoT (IIoT) scenarios. It supports research into the feasibility and overhead of quantum-resistant security protocols on resource-constrained industrial hardware.

Feb 06, 2026
Industrial IoT Kaggle

Voice To Shell: IoT Maintenance Task Datasets

Published in October 2025, this dataset includes command logs and sensor feedback for an SLM-based assistant designed for IoT maintenance tasks. It bridges voice commands with shell-level operations in industrial environments, facilitating the training of AI assistants for hardware management.

Feb 06, 2026
Industrial IoT Kaggle

Industrial IoT Dataset (Synthetic) for Predictive Maintenance

The Industrial IoT Dataset (Synthetic) provides a large-scale simulation of sensor readings and operational metrics from machines deployed in a smart factory environment. It focuses on predictive maintenance and anomaly detection.

Feb 05, 2026

Explore other topics

All topics →