Group control and identification of residential appliances using a nonintrusive method

Sunil SEMWAL; Munendra SINGH; Rai Sachindra PRASAD

Article Details

Group control and identification of residential appliances using a nonintrusive method

Authors:

Sunil SEMWAL | Munendra SINGH | Rai Sachindra PRASAD

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2015 Vol.23 Issue 6, pp.1805-1816

Document Type:

Article

Publication Date:

12/2015

Document Language:

Turkish

Document ID (Org No.):

2000742406

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

Abstract:

 

Identifying and controlling (ON/OFF) electrical appliance(s) from a remote location is an essential part of energy management. This motivated us to design a system that can collect the aggregate load signature from a single point, obtain the features, and finally identify the ON state of electrical appliance(s). The proposed disaggregation technique can be divided into two modules: the first part proposes an electrical installation system to disaggregate the appliance at the circuit level, whereas the second part consists of feature selection, dimension reduction, and classification algorithms. Load signatures of electrical appliances were combined with white Gaussian noise to analyze how noise affects the classification results. Amplitudes of the major eight harmonics of load signatures were selected as a feature for the classification. Various classification algorithms were applied to data to check their feasibility. The comparative evaluation showed that among the considered classifiers, the multilayer perceptron-artificial neural network (MLP-ANN) classifier leads in classification accuracy with 99.18{\%}. If the system is combined with noise, the accuracy decreases to 93.10{\%}. This paper also shows that the proposed technique reduces the space complexity and decision time of the smart meter.

 

Identifying and controlling (ON/OFF) electrical appliance(s) from a remote location is an essential part of energy management. This motivated us to design a system that can collect the aggregate load signature from a single point, obtain the features, and finally identify the ON state of electrical appliance(s). The proposed disaggregation technique can be divided into two modules: the first part proposes an electrical installation system to disaggregate the appliance at the circuit level, whereas the second part consists of feature selection, dimension reduction, and classification algorithms. Load signatures of electrical appliances were combined with white Gaussian noise to analyze how noise affects the classification results. Amplitudes of the major eight harmonics of load signatures were selected as a feature for the classification. Various classification algorithms were applied to data to check their feasibility. The comparative evaluation showed that among the considered classifiers, the multilayer perceptron-artificial neural network (MLP-ANN) classifier leads in classification accuracy with 99.18{\%}. If the system is combined with noise, the accuracy decreases to 93.10{\%}. This paper also shows that the proposed technique reduces the space complexity and decision time of the smart meter.

Article Details

Group control and identification of residential appliances using a nonintrusive method

Authors: Sunil SEMWAL | Munendra SINGH | Rai Sachindra PRASAD

Publisher: TUBITAK

Journal: Turkish Journal of Electrical Engineering and Computer Science 2015 Vol.23 Issue 6, pp.1805-1816

Document Type: Article

Publication Date: 12/2015

Document Language: Turkish

Document ID (Org No.): 2000742406

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

Sunil SEMWAL | Munendra SINGH | Rai Sachindra PRASAD

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2015 Vol.23 Issue 6, pp.1805-1816

Document Type:

Article

Publication Date:

12/2015

Document Language:

Turkish

Document ID (Org No.):

2000742406

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632