PMU-Based Residential Appliance Dataset (USPCASE, NUST)

The USPCASE–NUST Smart Grid and Power Lab Appliance Energy Dataset is a real-time electrical load monitoring dataset collected at the Smart Grid and Power Lab, USPCASE, National University of Sciences and Technology (NUST), Pakistan. The dataset was developed for research in Non-Intrusive Load Monitoring (NILM), smart grid analytics, appliance classification, load forecasting, and machine learning-based energy monitoring applications.

The data was acquired using a PMU/PDC-based monitoring infrastructure integrated with NI LabVIEW software. It contains timestamped measurements of appliance-level electrical parameters recorded under realistic operating conditions.

Dataset Summary
Total Records: 50,577
Total Features: 6
Time Span: August 2024 – March 2025
Data Type: Time-series electrical measurements
Environment: Real-time smart grid laboratory setup

Features
Date and Time – Timestamp of each measurement
Aggregated Power – Total observed power
Voltage – Operating voltage measurement
Current – Current consumption
Power – Real power consumed
Appliance – Appliance label/category

Appliances Included
TV
Microwave Oven
Refrigerator
Vacuum Cleaner
6 Ton AC
Iron
Electric Kettle
Hair Dryer
Toaster
Washing Machine
Coffee Machine
Desktop Computer
Research Applications

This dataset is suitable for:

NILM research
Appliance load classification
Smart home energy analytics
Deep learning and machine learning applications
Energy consumption prediction
Time-series forecasting
Load signature extraction
Real-time anomaly detection

Dataset Characteristics
Real-time measurements
Timestamped sequential data
Multiple appliance categories
Suitable for deep learning workflows
Realistic appliance operational behavior

Keywords

Smart Grid, NILM, PMU, PDC, Appliance Classification, Energy Consumption, Deep Learning, Load Monitoring, Time-Series Data, USPCASE, NUST