Abstract
Identification and monitoring of residential appliances are important facets for home energy management and essential for proper functioning of the connected devices. In this paper, residential appliances are identified using some pre-estimated statistical features which are derived from time sequence signal of electrical data. The extracted statistical features are then used to build a type-2 fuzzy system for proper identification of the household appliances. Internet-of-things (IoT) based load control utilizing power consumption data is also built for monitoring and control of the residential appliances remotely. The proposed non-intrusive load monitoring and identification technique has better identification accuracy than similar state-of-the-art techniques as observed from the comparison analysis. Furthermore, a laboratory scale prototype system is developed to validate the usefulness of the proposed load identification and monitoring technique.
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