Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring

Bergés, Mario; Goldman, Ethan; Matthews, H. Scott; Soibelman, Lúcio

doi:10.1111/j.1530-9290.2010.00280.x

Cited by 137 publications

(60 citation statements)

References 13 publications

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“…The 1-NN algorithm was compared to different classifiers (Gaussian naive Bayes, DTs and multiclass AdaBoost), and it obtained the best results of all with an accuracy of 79% over the validation set. In a later study [61], these results were completed, increasing the number of appliances up to 17, with similar results.…”

Section: Classification Problems and Algorithms In Appliance Load Monsupporting

confidence: 65%

“…Problem Specific Methodology Used [52] 2015 Power disturbance Classification SVM [53] 2013 Power disturbance Classification DT, ANN, neuro-fuzzy, SVM [54] 2014 Power disturbance Classification DT, SVM [55] 2014 Power disturbance Classification DT [56] 2012 Power disturbance Classification DT, DE [57] 2004 Power disturbance Classification Fuzzy expert, ANN [58] 2010 Power disturbance Classification Fuzzy classifiers [59] 2010 Power disturbance Classification GFS [48] 2006 Appliance load monitoring Classification ANN [60] 2009 Appliance load monitoring Classification k-NN, DTs, naive Bayes [61] 2010 Appliance load monitoring Classification k-NN, DTs, naive Bayes [62,63] 2010 Appliance load monitoring Classification LR, ANN [64] 2012 Appliance load monitoring Classification SVM [65] 2013 Solar Classification, regression SVM, ANN, ANFIS, wavelet, GA [66] 2008 Solar Classification, regression ANN, fuzzy systems, meta-heuristics [67] 2004 Solar Classification PNN [68] 2006 Solar Classification PNN [69] 2009 Solar Classification PNN, SOM, SVM [70] 2004 Solar Classification SVM [71] 2014 Solar Classification SVM [72] 2015 Solar Classification SVM [73] 2006 Solar Classification Fuzzy rules [74] 2013 Solar Classification Fuzzy classifiers [75] 2014 Solar Classification Fuzzy rules…”

Section: Ref Year Application Fieldmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.

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Section: Classification Problems and Algorithms In Appliance Load Monsupporting

confidence: 65%

Section: Ref Year Application Fieldmentioning

confidence: 99%

A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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“…Thus, different appliances might be recognized by different algorithms. Using this approach, Berges et al (2010) improved accuracy around 10% beyond other algorithms that used a similar frequency.…”

Section: Open Development Questionsmentioning

confidence: 98%

“…In Berges et al (2010), the authors build custom monitoring prototype using commodity hardware to collect data for one apartment, having in mind an application to personal energy auditing. They implement several existing NALM approaches, effectively experimenting with different appliance signatures proposed in the literature (real and reactive power, harmonics and transients) combined with off-the-shelf machine learning classification algorithms.…”

Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

Is disaggregation the holy grail of energy efficiency? The case of electricity

Gupta²,

et al. 2013

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This paper aims to address two timely energy problems. First, significant low-cost energy reductions can be made in the residential and commercial sectors, but these savings have not been achievable to date. Second, billions of dollars are being spent to install smart meters, yet the energy saving and financial benefits of this infrastructure -without careful consideration of the human element -will not reach its full potential. We believe that we can address these problems by strategically marrying them, using disaggregation. Disaggregation refers to a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or wholebuilding, energy signal. In this paper, we explain how appliance level data affords numerous benefits, and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data. We review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. Research, technology, and policy recommendations are also outlined.

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“…Discovering patterns from the whole-home electricity signal and then "factoring" these patterns into individual component appliances is called household energy disaggregation, and it has been an active research topic in recent years [30][36] [7] [26]. It can reveal how energy is consumed by what device, enabling users to take effective energy-saving steps.…”

Section: Weak Teaching Example: Energy Disaggregationmentioning

confidence: 99%

A general framework for never-ending learning from time series streams

Chen

Hao

Rakthanmanon

et al. 2014

Data Min Knowl Disc

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Abstract-Time series classification has been an active area of research in the data mining community for over a decade, and significant progress has been made in the tractability and accuracy of learning. However, virtually all work assumes a one-time training session in which labeled examples of all the concepts to be learned are provided. This assumption may be valid in a handful of situations, but it does not hold in most medical and scientific applications where we initially may have only the vaguest understanding of what concepts can be learned. Based on this observation, we propose a never-ending learning framework for time series in which an agent examines an unbounded stream of data and occasionally asks a teacher (which may be a human or an algorithm) for a label. We demonstrate the utility of our ideas with experiments that consider real-world problems in domains as diverse as medicine, entomology, wildlife monitoring, and human behavior analyses.

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Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring

Cited by 137 publications

References 13 publications

A Review of Classification Problems and Algorithms in Renewable Energy Applications

A Review of Classification Problems and Algorithms in Renewable Energy Applications

Is disaggregation the holy grail of energy efficiency? The case of electricity

A general framework for never-ending learning from time series streams

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