Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

Arash Moradzadeh, Omid Sadeghian, Kazem Pourhossein, Behnam Mohammadi-Ivatloo and Amjad Anvari-Moghaddam
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Arash Moradzadeh: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran
Omid Sadeghian: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran
Kazem Pourhossein: Department of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz 5157944533, Iran
Behnam Mohammadi-Ivatloo: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran
Amjad Anvari-Moghaddam: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran

Sustainability, 2020, vol. 12, issue 8, 1-14

Abstract: The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM.

Keywords: load disaggregation; non-intrusive load monitoring (NILM); dimensionality reduction; principal component analysis (PCA) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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