Data-Flow Programming based Non-Intrusive Load Monitoring for Electricity in Remote Area

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.