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

Abstract

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.