Data Management and Analytics

This dataset contains synthetic smart meter data with simulated cyber attacks, designed to support research in anomaly detection, cybersecurity, and energy consumption analysis. The dataset is based on 159 users from the Smart Meters in London dataset, selected for their regular consumption patterns. This larger dataset can be found in

https://www.kaggle.com/datasets/jeanmidev/smart-meters-in-london,

which is a refactorised version of the data found in

https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households.

The Machine Failure Predictions Dataset (D_2) is a real-world dataset sourced from Kaggle, containing 10,000 records and 14 features pertinent to IIoT device performance and health status. The binary target feature, 'failure', indicates whether a device is functioning (0) or has failed (1). Predictor variables include telemetry readings and categorical features related to device operation and environment. Data preprocessing included aggregating features related to failure types and removing non-informative features such as Product ID.

The PdM_telemetry Dataset (D_1) is a synthetic dataset designed to support predictive maintenance (PdM) research for IIoT (Industrial Internet of Things) devices by providing sensor-based telemetry data. This dataset initially comprises 97,210 records and 30 features, including a binary target feature, 'failure', which indicates whether a device will fail within the next 24 hours. The remaining features, such as device operational metrics and error counts, serve as predictors.