Author: Benedikt Herold, benedikt.herold@tuwien.ac.at
License: Creative Commons Attribution 4.0 International
DOI: 10.70124/xzk92-8mm08
DMP Reference: 10.5281/zenodo.17732654
This is an detailed description on the data provided via the csv files, which can be read with any csv-interpreter software.
Time is given in the standard python datetime format, all other values except ToI refer to power values with the unit W. ToI is unit less and a logical counter value.
This file contains the raw and processed power data for the aggregated profile and individual devices. The columns include time in Python datetime format, Raw_Power representing the unprocessed aggregated power data, Filtered_Power which is the median-filtered signal (window size 3) to reduce spikes while preserving sharp edges, and First_Order_Difference which is the first-order difference of the filtered signal to extract step sizes. Additionally, it contains the filtered measurements for individual devices Measured_Device_Power, and ToI is a boolean mask marking Timestamps of Interest (ToIs) that were detected.
This file captures the generated event maps based on different matching score thresholds. The columns include time in Python datetime format, High_Threshold , Optimal_Threshold, and Low_Threshold, representing the event maps generated at corresponding confidence levels, and ToI, a boolean mask indicating Timestamps of Interest (ToIs) detected in the event maps.
This file provides reconstructed and filtered measurements for both aggregated and individual devices. The columns include time in Python datetime format, Filtered_Aggregated_Power representing the filtered aggregated measurements, Reconstructed_Power which is the reconstructed power profile of detected devices, Measured_Power for the filtered measurements of individual devices, Remaining_Unlabeled_Power indicating remaining unlabeled power, and ToI, a boolean mask marking Timestamps of Interest (ToIs) that were detected.
For generation of the described dataset execute the provided python file edge_detection.py without any additional arguments.
In order du run the script following libraries are required: