In this research, we analyze and model thermal and electrical energy loads of
energy transformer stations with the help of machine learning and numerical
methods. Transformer stations are a key part of the electrical power system.
They are the elements that connect energy sources to end-users. Because
of an ever-increasing amount of transformer station overloads, this thesis
focuses on analyzing and modeling thermal and electrical loads. For this
reason, transformer stations have been equipped with temperature sensors.
We combined transformer station temperature data with weather and energy
usage data. We used multiple machine learning algorithms to predict elec-
trical energy consumption. The best results were obtained by random forest
and support vector machines. Our research results are forecasting models
that can be combined with expert domain knowledge to predict transformer
station overloads.
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