Comprehensive feature selection for appliance classification in NILM
Introduction
A breakdown of energy consumption at the appliance level is not only an essential requirement for energy providers, in designing practical demand response algorithms (e.g., taking into account human behavioral uncertainties, or targeting specific user and appliance groups), but it also benefits residential customers by providing them the necessary information for improving their energy consumption efficiency [1]. Non-intrusive (appliance) load monitoring (NILM) techniques are cost-effective solutions to obtain such information. The general framework of NILM starts from input measurements of total electricity consumption to eventually disaggregate it into the individual contributions of each load.
A crucial step in NILM is feature extraction, which applies signal processing techniques to extract features from voltage (V) and current (I) measurements. The ultimate goal of the feature extraction step is to derive a signature (using a feature or combination of features) that can uniquely identify the individual appliances. The performance of any NILM system depends on the uniqueness of the appliance signature compared to that of other devices. Hence, identification of such signature is crucial in improving the load discrimination capability of a NILM system. Although NILM has been the subject of research for over two decades, so far a systematic selection of the various electrical features proposed for effective discrimination of loads has not yet been presented. Identifying the most meaningful set of electrical parameters to distinguish all appliances still remains one of the major challenges in NILM [42]. In this paper, we tackle this issue and contribute with:
- 1.
a concise and up-to-date review of the features reported in recent NILM literature (Section 2) and
- 2.
a systematic signature identification algorithm based on a comprehensive dataset with diverse appliances and various households (Section 3.1).
Section snippets
State of the art on feature extraction
The seminal work by Hart [2], has inspired extensive research on extracting features and developing discriminating algorithms for NILM purposes. Zeifman and Roth [3] and Zoha et al. [4] provide an extensive overview of features and algorithms that were proposed before 2012. In this section, a concise and updated review is provided that incorporates the latest developments in the state-of-the-art on feature extraction.
The type of features that can be extracted from voltage (V) and current (I)
Methodology
In this section, a systematic approach is presented to identify and combine features in such a way that the ability to distinguish various loads is maximized.
A comprehensive list of steady and transient state features, including references to their extraction steps is outlined in Table 2 and Appendix A.
The feature selection process starts from all features and iteratively eliminates the least important ones until a reduced subset is obtained. Note that the goal is not to reduce number of
Results and discussion
In this section, we demonstrate our systematic feature selection process and present the per appliance based analysis using a comprehensive dataset explained next.
Conclusion
The effectiveness of a NILM algorithm to distinguish between appliances largely depends on determining a set of discriminative features. Various research has focused on suggesting and extracting such features to classify appliances. As a first contribution, we provided a systematic listing and comparison between features that have been proposed.
As a second contribution, we constructed and suggested an optimal subset of features to be used in a computational model which can achieve top
Future work
The majority of the proposed NILM solutions are tested on private datasets with a limited number of appliances. Additionally, the existing literature categorizes appliances into three categories based on their operational characteristics: ON/OFF appliances, multiple state appliances, and variable load appliances. However, when it comes to identifying a unique signature for each appliance, such categorization is not very effective because it ignores the front-end circuit topology of the
References (55)
- et al.
Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms
Energy Build.
(1996) - et al.
Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses
Energy Build.
(1999) - et al.
Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings
Energy Convers. Manag.
(2000) - et al.
A nonintrusive load identification method for residential applications based on quadratic programming
Electr. Power Syst. Res.
(2016) - et al.
Is disaggregation the holy grail of energy efficiency? The case of electricity
Energy Policy
(2013) Nonintrusive appliance load monitoring
Proc. IEEE
(1992)- et al.
Nonintrusive appliance load monitoring: review and outlook
IEEE Trans. Consumer Electron.
(2011) - et al.
Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey
Sensors
(2012) - et al.
Data extraction for effective non-intrusive identification of residential power loads
IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No. 98CH36222)
(1998) - et al.
Algorithm for nonintrusive identification of residential appliances
Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, 1998, ISCAS’98
(1998)
Using a rule-based algorithm to disaggregate end-use load profiles from premise-level data
IEEE Comput. Appl. Power
Nonintrusive appliance load monitoring based on an optical sensor
2003 IEEE Bologna Power Tech Conference Proceedings
Detecting patterns of appliances from total load data using a dynamic programming approach
Fourth IEEE International Conference on Data Mining, 2004, ICDM’04
Genetic algorithm for pattern detection in nialm systems
IEEE International Conference on Systems, Man and Cybernetics, 2004, vol. 4
Real-time recognition and profiling of appliances through a single electricity sensor
2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON)
An Experimental Study on Electrical Signature Identification of Non-Intrusive Load Monitoring (NILM) Systems
Using appliance signatures for monitoring residential loads at meter panel level
IEEE Trans. Power Deliv.
Neural-network-based signature recognition for harmonic source identification
IEEE Trans. Power Deliv.
Power signature analysis
IEEE Power Energy Mag.
Learning systems for electric consumption of buildings
ASCE International Workshop on Computing in Civil Engineering
Enhancing electricity audits in residential buildings with nonintrusive load monitoring
J. Ind. Ecol.
Non-intrusive signature extraction for major residential loads
IEEE Trans. Smart Grid
Transient event detection in spectral envelope estimates for nonintrusive load monitoring
IEEE Trans. Power Deliv.
Nonintrusive load monitoring and diagnostics in power systems
IEEE Trans. Instrum. Meas.
Estimation of variable-speed-drive power consumption from harmonic content
IEEE Trans. Energy Convers.
Harmonics load signature recognition by wavelets transforms
DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382)
Non-intrusive load monitoring based on switching voltage transients and wavelet transforms
2012 Future of Instrumentation International Workshop (FIIW) Proceedings
Cited by (138)
A low complexity binary-weighted energy disaggregation framework for residential electricity consumption
2023, Energy and BuildingsElectric energy disaggregation via non-intrusive load monitoring: A state-of-the-art systematic review
2022, Electric Power Systems ResearchPrinciples, research status, and prospects of feature engineering for data-driven building energy prediction: A comprehensive review
2022, Journal of Building Engineering