Cihaz seviyesinde enerji ayrıştırma için gözetimsiz yöntemlerin geliştirilmesi

Detailed feedback on electricity consumption encourages users to save energy, thereby reducing energy waste and consequently carbon emissions. However, measuring the individual consumption of each device connected to an internal electrical wiring system is costly and technically challenging. Today, many electricity meters have communication capabilities and can report measurements at least once per minute. By processing electricity meter data that covers a wide measurement range using data mining techniques, it may be possible to estimate the disaggregated consumption time series of individual devices and provide users with more insightful feedback. This thesis aims to develop an unsupervised, fast, and easily interpretable load disaggregation method that can also be applied to meter data with low reporting frequency. The method consists of several steps: data preprocessing, change point detection, feature extraction, clustering, and postprocessing. In a case study using electricity consumption data collected over more than three months from a single household, the consumption of certain devices was successfully disaggregated. The findings indicate that the proposed method can be applied even in resource-constrained environments and is capable of producing meaningful results.