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 there are several procedures / steps taken to make a neuron that can recognize and decide on an action. The procedure is training 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. Load classification method, where 1 for TV load classification, 2 for fan load, 3 for ironing load, 4 for water pump load, 5 for lamp load, 6 for dispenser load, and 7 for load combination of fan iron. The total load is 6 single loads and 1 combination load. One load combination is chosen because, on the combination load characteristics when the fan has characteristics that 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 can be seen, that the biggest training error is found in the seventh data, which is the identification of the load in the fan-iron load classification. This is because the current pattern on the iron and fan with the iron or fan itself has almost the same characteristics. However, for this process networks will be used and then the PSO optimization method is used to reduce the error, in the next study. From the test results it is shown that by varying the input data of each load, networks have been able to identify well.
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