Probability density-based energy-saving recommendations for household refrigerating appliances

F. Rodríguez-Cuenca, E.F. Sánchez-Úbeda, J. Portela, A. Muñoz, V. Guizien, A. Veiga, A. Mateo

9th International Conference on Time Series and Forecasting - ITISE 2023, Las Palmas de Gran Canaria (Spain). 12-14 julio 2023

Summary:

The power sector is a major contributor to anthropogenic global warming, responsible for 38% of total energy-related carbon dioxide emissions and 66% of carbon dioxide emission growth in 2018. In OECD member countries, the residential sector consumes a significant amount of electrical energy, with household refrigerating appliances alone accounting for 30-40% of the total consumption. To analyze the energy use of each domestic appliance, researchers have developed Appliance Level Energy Characterization (ALEC), a set of techniques that provide insights into individual energy consumption patterns. This study proposes a novel methodology that utilizes robust probability density estimation to detect refrigerators with high energy consumption and recommend tailored energy-saving measures. The methodology considers two consumption features: base energy consumption (energy usage without human interaction) and relative energy consumption (energy usage influenced by human interaction). To assess the approach’s effectiveness, the methodology was tested on a dataset of 30 different appliances from monitored homes, yielding positive results that support the robustness of the proposed method.

Spanish layman's summary:

Este estudio propone una nueva metodología para detectar frigoríficos con un elevado consumo energético y recomendar medidas de ahorro de energía a medida,  mediante la estimación de densidad de probabilidad. La metodología tiene en cuenta dos características de consumo: el consumo de energía base (consumo de energía sin interacción humana) y el consumo de energía relativo (consumo de energía influido por la interacción humana).

English layman's summary:

This study proposes a novel methodology that utilizes robust probability density estimation to detect refrigerators with high energy consumption and recommend tailored energy-saving measures. The methodology considers two consumption features: base energy consumption (energy usage without human interaction) and relative energy consumption (energy usage influenced by human interaction).

Keywords: Household Refrigerating Appliances · Energy-Saving Recommendations · Appliance Level Energy Characterization

DOI: 10.3390/engproc2023039043

Publication date: July 2023.

Citation:
Rodríguez-Cuenca, F., Sánchez-Úbeda, E.F., Portela, J., Muñoz, A., Guizien, V., Veiga, A., Mateo, A., Probability density-based energy-saving recommendations for household refrigerating appliances, 9th International Conference on Time Series and Forecasting - ITISE 2023, Las Palmas de Gran Canaria (Spain). 12-14 July 2023.

IIT-23-045C