RANCANG BANGUN SMART METER BERBASIS NILM UNTUK MEMANTAU PEMAKAIAN ENERGI LISTRIK PADA SEKTOR RUMAH TANGGA MENGGUNAKAN NEURAL NETWORK

Abstract

In this study carried out using household electrical loads, such as televisions, lights, water pumps, irons, fans,and dispensers. The use of the Neural Network algorithm is used as a load identification method. In its application thereare several procedures / steps taken to make a neuron that can recognize and decide on an action. The procedure istraining and neuron testing to be made. Matlab software has a Neural Network tool, which in this study will be used.Load sampling data is used as input data for neural network training. As output / target load classification is used. Loadclassification method, where 1 for TV load classification, 2 for fan load, 3 for ironing load, 4 for water pump load, 5 forlamp load, 6 for dispenser load, and 7 for load combination of fan iron. The total load is 6 single loads and 1 combinationload. One load combination is chosen because, on the combination load characteristics when the fan has characteristicsthat are not the same as the others. The sampling of current data for each load will be used as neural network training.Load data used is 30 samples or for 30 seconds, with each minute the data is taken. From the results of the training it canbe seen, that the biggest training error is found in the seventh data, which is the identification of the load in the fan-ironload classification. This is because the current pattern on the iron and fan with the iron or fan itself has almost the samecharacteristics. However, for this process networks will be used and then the PSO optimization method is used to reducethe error, in the next study. From the test results it is shown that by varying the input data of each load, networks havebeen able to identify well.