Abstract

Industrial Kitchens (IKs) are characterized by high energy consumption, yet they remain largely overlooked in energy research. Understanding how electricity is used in IKs is crucial for identifying opportunities for energy optimization and improving sustainability in this sector. This paper presents a data-driven methodology for analyzing appliance consumption by automatically detecting and classifying appliance activations. The approach combines automatic activity detection with unsupervised clustering to reveal usage patterns. Evaluated on data from nine IK appliances, the methodology achieves outstanding performance, with average balanced accuracy and F1-scores exceeding 0.98. The unsupervised classification identifies distinct cycle modes for each appliance, with the optimal number of clusters varying across appliances. Load fluctuation patterns are found to be the most significant feature, with appliances like the ice machine exhibiting unique consumption behaviors compared to similar appliances like refrigerators. In contrast, appliances such as the salamander draw power consistently, regardless of activity duration. These findings not only contribute to a better understanding of energy use in IKs but also lay the groundwork for future research on demand response strategies and energy efficiency improvements in small-scale commercial kitchens.