Teleworking monitoring system using NILM and K-NN algorithms: a strategy for sustainable smart cities - Yang, Chuan Choong; Noh, Adriana; Ibrahim, Siti Noorjannah; Asnawi, Ani Liza; Mohamed Azmin, Nor Fadhillah (IIUM Press, 2024)
Working from home or teleworking has become a common practice for most office employees during certain special situations such as pandemic. One of the challenges faced by employers, however, is monitoring workers who are working from home. Webcam, live video feed, or mobile phone tracking deemed to be intrusive. Therefore, in this work, a non-intrusive monitoring approach is used to effectively help employers to keep track of teleworking employees through specific electrical appliances operating condition while maintaining users’ privacies. This strategy uses non-intrusive load monitoring (NILM) approach to recognize four electrical appliances’ switching events used during teleworking measured from a single power point. Together with an event classification method known as K-Nearest Neighbor (k-NN) algorithm, the teleworking event and duration can be identified.The results were presented using classification metrics that consist of confusion matrix andaccuracy score. An accuracy of up to 62% has been achieved for the classifier. It is observedthat the similarity of appliances’ power usage affects the model accuracy and confusion matrixis constructed to help identify the number of events that are correctly classified as well aswrongly classified. Results from NILM and k-NN strategy can be implemented in the smartcity towards sustainability to create a sustainable and employees well-being. It is also usefulfor an organization to evaluate an employee’s performance who opt for teleworking.