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

Author

Listed:
  • 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
    Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam)

  • Amjad Anvari-Moghaddam

    (Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran
    Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

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.

Suggested Citation

  • Arash Moradzadeh & Omid Sadeghian & Kazem Pourhossein & Behnam Mohammadi-Ivatloo & Amjad Anvari-Moghaddam, 2020. "Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis," Sustainability, MDPI, vol. 12(8), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3158-:d:345442
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    Citations

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    Cited by:

    1. Kh Md Nahiduzzaman & Abdullatif Said Abdallah & Arash Moradzadeh & Amin Mohammadpour Shotorbani & Kasun Hewage & Rehan Sadiq, 2023. "Impacts of Tariffs on Energy Conscious Behavior with Respect to Household Attributes in Saudi Arabia," Energies, MDPI, vol. 16(3), pages 1-24, February.
    2. Arash Moradzadeh & Sahar Zakeri & Maryam Shoaran & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    3. Esmaeil Ahmadi & Younes Noorollahi & Behnam Mohammadi-Ivatloo & Amjad Anvari-Moghaddam, 2020. "Stochastic Operation of a Solar-Powered Smart Home: Capturing Thermal Load Uncertainties," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    4. Oscar G. Duarte & Javier A. Rosero & María del Carmen Pegalajar, 2022. "Data Preparation and Visualization of Electricity Consumption for Load Profiling," Energies, MDPI, vol. 15(20), pages 1-30, October.
    5. Tomas Macak & Jan Hron & Jaromir Stusek, 2020. "A Causal Model of the Sustainable Use of Resources: A Case Study on a Woodworking Process," Sustainability, MDPI, vol. 12(21), pages 1-22, October.
    6. Hossein Moayedi & Bao Le Van, 2022. "Feasibility of Harris Hawks Optimization in Combination with Fuzzy Inference System Predicting Heating Load Energy Inside Buildings," Energies, MDPI, vol. 15(23), pages 1-17, December.
    7. Omid Sadeghian & Arash Moradzadeh & Behnam Mohammadi-Ivatloo & Mehdi Abapour & Fausto Pedro Garcia Marquez, 2020. "Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach," Energies, MDPI, vol. 13(11), pages 1-25, June.
    8. Bilal Naji Alhasnawi & Basil H. Jasim & Walid Issa & Amjad Anvari-Moghaddam & Frede Blaabjerg, 2020. "A New Robust Control Strategy for Parallel Operated Inverters in Green Energy Applications," Energies, MDPI, vol. 13(13), pages 1-31, July.
    9. Carles Manera & Eloi Serrano & José Pérez-Montiel & Màrian Buil-Fabregà, 2021. "Construction of Biophysical Indicators for the Catalan Economy: Building a New Conceptual Framework," Sustainability, MDPI, vol. 13(13), pages 1-20, July.
    10. Arash Moradzadeh & Sahar Zakeri & Waleed A. Oraibi & Behnam Mohammadi-Ivatloo & Zulkurnain Abdul-Malek & Reza Ghorbani, 2022. "Non-Intrusive Load Monitoring of Residential Loads via Laplacian Eigenmaps and Hybrid Deep Learning Procedures," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    11. İsmail Hakkı Çavdar & Vahit Feryad, 2021. "Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid," Energies, MDPI, vol. 14(15), pages 1-21, July.
    12. Elnaz Azizi & Mohammad T. H. Beheshti & Sadegh Bolouki, 2021. "Event Matching Classification Method for Non-Intrusive Load Monitoring," Sustainability, MDPI, vol. 13(2), pages 1-20, January.
    13. Jarosław Brodny & Magdalena Tutak & Peter Bindzár, 2021. "Assessing the Level of Renewable Energy Development in the European Union Member States. A 10-Year Perspective," Energies, MDPI, vol. 14(13), pages 1-38, June.

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