Search

Current filters:

Current filters:

Author

Subject

Date issued

Has File(s)

Search Results

Results 1-1 of 1 (Search time: 0.001 seconds).
  • <<
  • 1
  • >>
  • Authors: Seidu Agbor Abdul, Rauf; Adebayo F., Adekoya;  Advisor: -;  Co-Author: - (2023)

    The demand for electricity at home has increased in recent times globally, this high demand for continuous, stable and affordable power can be attributed to the demand for comfortable lifestyle of consumers but the quality and efficiency of the appliances being used remain questionable. Malfunctioning appliances usually show a power signature statistically different from their normal behavior, which can lead to higher energy consumption or more serious damages. As a result, numerous studies in recent times have been conducted on the household electrical appliance anomaly behaviors to find the root-cause of these anomalies using machine learning techniques and algorithms.