Simple Event Detection and Disaggregation Approach for Residential Energy Estimation

Non-Intrusive Appliances Load Monitoring systems are crucial for augmenting virtuous energy saving behavior and providing residential energy monitoring solutions. NILM is aimed to accurately account for energy costs and load distribution by reporting where exactly the energy is going and what kind of devices are using it. To address this issue, much of existing methods require a lot of time consuming training, complex optimization algorithms and does not focus on the actual energy estimation problem rather concentrate on classification problem. In this paper, we propose a simple active window based NILM (AWB-NILM) approach that relies on an unsupervised localized events clustering, pairing and self-learning using automatic evolutionary clustering methods. We have tested our approach on a real residential power consumption data. The F-measure attained by the event detector is 94.6%. In this paper we showed accuracies of appliance events correctly classified and energy correctly assigned.

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