MODEL KOMPUTASI CERDAS UNTUK KLASIFIKASI TINGGI RENDAH DAYA LISTRIK PADA GARDU MENGGUNAKAN METODE LEARNING VECTOR QUANTIZATION (LVQ)
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
SEMI, MIFTAUL JATZIA
Indrianto, Indrianto
Haris, Abdul
Indrianto, Indrianto
Haris, Abdul
Subject
Teknik Informatika
Datestamp
2023-05-31 07:39:31
Abstract :
The increasing demand and number of consumers is directly proportional to the increasing demand for electrical loads from time to time. If demand is greater than supply or vice versa, there will be a waste of electricity and system failure. the classification of the use of short-term electrical power loads is needed for control and consideration in scheduling maintenance of the electric power system in an effort to reduce problems that arise due to imbalances in demand and supply. distributed and stored for short-, medium- and long-term needs. The Learning Vector Quantization method is an artificial neural network that is used for grouping inputs to be given and learning at the competitive layer that is automatically supervised and is one of the supervised learning methods that can be implemented in the problem of classifying the use of power loads at electrical substations. This system will manage data in the form of electricity consumption in June 2020 as training data and test data using data in the following month. The parameters used to get the best accuracy results are learning rate 0.01, hidden 10 and epoch 100 with a total of 30 days of data. Based on the test results, the accuracy in June was 96,6667%. By using the June 2020 substation power load data, it can be applied as a reference for classification in the Learning Vector Quantization method for power load classification at electrical substations.
The increasing demand and number of consumers is directly proportional to the increasing demand for electrical loads from time to time. If demand is greater than supply or vice versa, there will be a waste of electricity and system failure. the classification of the use of short-term electrical power loads is needed for control and consideration in scheduling maintenance of the electric power system in an effort to reduce problems that arise due to imbalances in demand and supply. distributed and stored for short-, medium- and long-term needs. The Learning Vector Quantization method is an artificial neural network that is used for grouping inputs to be given and learning at the competitive layer that is automatically supervised and is one of the supervised learning methods that can be implemented in the problem of classifying the use of power loads at electrical substations. This system will manage data in the form of electricity consumption in June 2020 as training data and test data using data in the following month. The parameters used to get the best accuracy results are learning rate 0.01, hidden 10 and epoch 100 with a total of 30 days of data. Based on the test results, the accuracy in June was 96,6667%. By using the June 2020 substation power load data, it can be applied as a reference for classification in the Learning Vector Quantization method for power load classification at electrical substations.
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