  This article presents a new methodology for extracting intervals when a home
is vacant from low-frequency electricity consumption data. The approach
combines multiple algorithms, including change point detection, classification,
period detection, and periodic spikes retrieval. It shows encouraging results
on both simulated and real consumption curves. This approach offers practical
insights for optimizing energy use and holds potential benefits for residential
consumers and utility companies in terms of energy cost reduction and
sustainability. Further research is needed to enhance its applicability in
diverse settings and with larger datasets.
