The supply of electricity to Slovenia is closely connected with the maintenance of all existing portable facilities in Slovenia. In this B.A. thesis, we analyse the RTP Divača facility, whose very important building blocks are two phase-shifting transformer with taps, which control voltage and power regulation. The checks of these two transformers are carried out in regard to time or the state of the transformer. In the thesis, the number of switches per day, per week and per month is modelled with the help of machine learning. The models were trained based on the data measured in the time period from 2017 to 2019, and their errors were evaluated on the basis of the data measured in the period from 2020 to 2021. The results of the test data show that for daily forecasting the number of switches LSTM is the most successful model, while for weekly and monthly forecasting the number of switches a linear regression model is best.
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