Dataset for Paper "A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring"

Dataset for Paper "A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring"

Citation Author(s):
Zuyi
Li
Illinois Institute of Technology
Mengqi
Lu
Illinois Institute of Technology
Submitted by:
Zuyi Li
Last updated:
Thu, 05/09/2019 - 15:56
DOI:
10.21227/1q59-6f15
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Abstract: 

This dataset is for the figures and the plots within the figures in the submitted paper "A hybrid event detection approach for non-intrusive load monitoring."

Non-Intrusive Load Monitoring (NILM) is a practical method to provide appliance-level electricity consumption information. Event detection, as an important part of event-based NILM methods, has a direct impact on the accuracy of the ultimate load disaggregation results in the entire NILM framework. This paper presents a hybrid event detection approach for relatively complex household load datasets that include appliances with long transients, high fluctuations, and/or near-simultaneous actions. The proposed approach includes a base algorithm based on moving average change with time limit, and two auxiliary algorithms based on derivative analysis and filtering analysis. The structure, steps, and working principle of this approach are described in detail. The proposed approach does not require additional information about household appliances, nor does it require any training sets. Case studies on different datasets are conducted to evaluate the performance of the proposed approach in comparison with several existing approaches including log likelihood ratio detector with maxima (LLD-Max) approach, active window-based (AWB) approach, and generalized likelihood ratio (GLR) approach. Results show that the proposed approach works well in detecting events in complex household load datasets and performs better than the existing approaches.

Instructions: 

1. Figure plot folder includes high resolution version of all figures except Fig. 5 in the submitted paper "A hybrid event detection approach for non-intrusive load monitoring."

2. Figure data floder includes the data used for the figures and the plots within the figures in the submitted paper "A hybrid event detection approach for non-intrusive load monitoring."

File Fig_x.y means the data for the yth plot of Fig. x. For example, Fig_11.2 means the data for the second plot of Fig. 11.

All the data files have two columns for time and active power respectively. The data does not contain the events data. Some figures containmultiple plots with the same aggregated data of different events, in which case only one set of aggregated data is included.   

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[1] Zuyi Li, Mengqi Lu, "Dataset for Paper "A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring"", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/1q59-6f15. Accessed: Sep. 18, 2019.
@data{1q59-6f15-19,
doi = {10.21227/1q59-6f15},
url = {http://dx.doi.org/10.21227/1q59-6f15},
author = {Zuyi Li; Mengqi Lu },
publisher = {IEEE Dataport},
title = {Dataset for Paper "A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring"},
year = {2019} }
TY - DATA
T1 - Dataset for Paper "A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring"
AU - Zuyi Li; Mengqi Lu
PY - 2019
PB - IEEE Dataport
UR - 10.21227/1q59-6f15
ER -
Zuyi Li, Mengqi Lu. (2019). Dataset for Paper "A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring". IEEE Dataport. http://dx.doi.org/10.21227/1q59-6f15
Zuyi Li, Mengqi Lu, 2019. Dataset for Paper "A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring". Available at: http://dx.doi.org/10.21227/1q59-6f15.
Zuyi Li, Mengqi Lu. (2019). "Dataset for Paper "A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring"." Web.
1. Zuyi Li, Mengqi Lu. Dataset for Paper "A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring" [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/1q59-6f15
Zuyi Li, Mengqi Lu. "Dataset for Paper "A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring"." doi: 10.21227/1q59-6f15