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A soft sensor to assess the energy performance of laundry washing machines

Author

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  • Jasiūnas, Žygimantas
  • Julião, Tiago
  • Cecílio, José
  • Carrilho da Graça, Guilherme
  • Ferreira, Pedro M.

Abstract

In the European Union (EU), domestic consumers buy over 15 million laundry washing machines annually, contributing to around 5% of the total domestic electricity consumption. This paper proposes and evaluates an IoT-based, low-cost soft sensor method for estimating the laundry load in domestic washing machines, enabling the assessment of washing machines’ real-life energy performance based on resource consumption efficiency. The methodology uses linear regression and artificial intelligence techniques to estimate load mass based on energy and water supply. The real-life assessment considers performance indicators expressing the energy and water resources used per kilogram of laundry load washed. The water-energy nexus combines these in a single energy performance indicator. The soft sensor is tested on various washing machine models, focusing on the commonly used ‘Cotton’ washing program, varying the washing temperature and the laundry load mass. A mean absolute error of 307 g and a corresponding root mean square error of 570 g was achieved, resulting in performance indicators mean absolute error of 5.89 Whkg (energy), 0.53 Lkg (water), and 6.30 Whkg for the combined water-energy nexus. This approach can be implemented in real-world settings to recommend optimal laundry loads and washing practices tailored to specific washing machines and users, maximizing energy savings.

Suggested Citation

  • Jasiūnas, Žygimantas & Julião, Tiago & Cecílio, José & Carrilho da Graça, Guilherme & Ferreira, Pedro M., 2025. "A soft sensor to assess the energy performance of laundry washing machines," Applied Energy, Elsevier, vol. 383(C).
    • Handle: RePEc:eee:appene:v:383:y:2025:i:c:s0306261925000790
    DOI: 10.1016/j.apenergy.2025.125349
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    References listed on IDEAS

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    1. Cabeza, Luisa F. & Ürge-Vorsatz, Diana & Ürge, Daniel & Palacios, Anabel & Barreneche, Camila, 2018. "Household appliances penetration and ownership trends in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 1-8.
    2. Liu, Ariane & Giurco, Damien & Mukheibir, Pierre, 2015. "Motivating metrics for household water-use feedback," Resources, Conservation & Recycling, Elsevier, vol. 103(C), pages 29-46.
    3. Giuliano Zambonin & Fabio Altinier & Alessandro Beghi & Leandro dos Santos Coelho & Nicola Fiorella & Terenzio Girotto & Mirco Rampazzo & Gilberto Reynoso-Meza & Gian Antonio Susto, 2019. "Machine Learning-Based Soft Sensors for the Estimation of Laundry Moisture Content in Household Dryer Appliances," Energies, MDPI, vol. 12(20), pages 1-24, October.
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