| --- |
| license: apache-2.0 |
| --- |
| --- |
| tags: |
| - energy disaggregation |
| - non-intrusive load monitoring |
| - time series |
| - electrical load monitoring |
| license: unknown # License information was not explicitly stated in the paper, might need clarification. |
| language: |
| - en |
| pretty_name: BLUED (Building-Level fUlly-labeled dataset for Electricity Disaggregation) |
| --- |
| |
| # Dataset Card for BLUED |
| |
| ## Dataset Description |
| |
| BLUED (Building-Level fUlly-labeled dataset for Electricity Disaggregation) is a public dataset designed for event-based Non-Intrusive Load Monitoring (NILM) research. It contains high-frequency voltage and current measurements from a single-family home in the United States over one week. The key feature of this dataset is the detailed labeling of appliance state transitions (events), providing ground truth for evaluating event-based disaggregation algorithms. The dataset aims to facilitate the development, testing, and comparison of NILM algorithms. |
| |
| ## Dataset Details |
| |
| * **Data Collection:** |
| * Data was collected over one week in October 2011 from a single-family house in Pittsburgh, Pennsylvania. |
| * Aggregate voltage and current measurements were captured at the main distribution panel using a National Instruments DAQ (NI USB-9215A) at a sampling rate of 12 kHz. Current was measured using split-core current transformers, and voltage was measured using a voltage transformer. |
| * Ground truth for appliance events was collected using a combination of plug-level power meters (FireFly sensors), environmental sensors (light, sound, vibration, etc.), and circuit-level current measurements. |
| * Events were defined as changes in power consumption greater than 30 watts lasting at least 5 seconds. |
| * Timestamps for ground truth events were manually synchronized with the aggregate power signal via visual inspection. |
| * **Data Content:** |
| * Raw voltage (one phase) and current (two phases) waveforms sampled at 12 kHz. |
| * Computed active power at 60 Hz. |
| * A list of timestamped events, identifying the appliance and the transition type (e.g., on/off). |
| * Covers approximately 50 electrical appliances, though not all were active or met the event criteria during the collection week. |
| * Includes 2,482 labeled events in total, with 2,355 attributed to known appliances and 127 from unknown sources (clustered into potentially 11 distinct appliances). Events are split between Phase A (904 events) and Phase B (1578 events). |
| * **Data Format:** Raw current and voltage files, along with a list of event timestamps. Active power computed at 60Hz is also included. |
| * **Data Splits:** The paper presents preliminary results using the whole week but suggests future work might involve splitting into training/testing sets. |
| |
| ## Uses |
| |
| * **Non-Intrusive Load Monitoring (NILM):** Primarily designed for developing and evaluating event-based energy disaggregation algorithms. |
| * **Appliance Usage Pattern Analysis:** Studying how and when different appliances are used in a residential setting. |
| * **Occupancy Detection:** Inferring household occupancy based on appliance usage. |
| * **Energy Management & Efficiency:** Developing strategies for residential energy savings. |
| * **Anomaly Detection & Fault Diagnostics:** Identifying unusual appliance behavior or potential faults. |
| * **Assisted Living Applications:** Monitoring activities of daily living through appliance usage. |
| |
| ## Dataset Limitations |
| |
| * **Duration:** One week of data may not capture the usage patterns of all appliances, especially seasonal ones (like the air conditioner) or those used infrequently (like the dryer). |
| * **Sensor Frequency Limitation:** The current sensors used had a cutoff frequency around 300 Hz, limiting the analysis of higher-frequency harmonics (beyond the 5th harmonic). |
| * **Incomplete Ground Truth:** Approximately 5% of events detected in the aggregate signal could not be attributed to the monitored appliances and are labeled as "unknown". Some appliances (~25%) had no registered events meeting the criteria during the collection week. |
| * **Single Home:** Data represents only one specific home and its occupants' behavior. |
| |
| ## Citation |
| |
| ```bibtex |
| @inproceedings{anderson2012blued, |
| title={BLUED: A fully labeled public dataset for event-based non-intrusive load monitoring research}, |
| author={Anderson, Kyle and Ocneanu, Adrian and Benitez, Diego and Carlson, Derrick and Rowe, Anthony and Berg{\'e}s, Mario}, |
| booktitle={Proceedings of the 2nd ACM SIGKDD international workshop on data mining applications in sustainability}, |
| pages={1--8}, |
| year={2012}, |
| organization={ACM} |
| } |
