The use of electrical energy in remote areas must be carried out efficiently. This
study proposed a load monitoring method in remote areas. This study developed a
Non-Intrusive Load Monitoring (NILM) based on Data-Flow Programming
(DFP) by applying a bagging decision tree algorithm to conduct load
disaggregation. This study built the DFP and the Graphical User Interface (GUI)
in LabVIEW connected to power sensor ADE9153A on Arduino UNO via serial
communication. This experiment was conducted on the LabVIEW 2020 running
on an Intel i5 2400-3.1 GHz CPU, 16 GB RAM, and 64-bit operating system
computer. This study produced a good performance of NILM with 0.9617 of
accuracy and 0.9728 of f1-score. The proposed method of the NILM process was
suitable for electricity in remote areas because the DFP used in the algorithm is
easy to understand, easy to operate, and inexpensive to build. Finally, the NILM
technique can improve the efficiency of used electrical energy in remote areas. By
applying NILM, the operator can determine the priority of which devices should
be ON or OFF at a particular time as needed. In addition, the NILM can
contribute to the balancing of small and weak microgrids in scenarios of high
renewable energy penetration.
Keywords
Data flow programming, Electricity in remote areas, LabVIEW, Load disaggregation, NILM.