Transformers that supply areas with electricity wear down over time as a result of fluctuating energy demands. This thesis focuses on the selection of the models and variables for deep learning and machine learning methods, particularly when used for electricity demand forecasts in transformers. By training and evaluating models on large datasets, we can compare the accuracy of the results.

Our study examined what models were the most commonly top performing in the field of transformer load forecasting and subsequently compared Random Forest Regression, Extreme Gradient Boost, and Long Short-Term Memory at 50 different three day intervals and found that LSTM provided the most accurate results. The most influential variables in LSTM were air and sea temperature. Our findings show that there is potential for artificial intelligence to be used in transformer load forecasts.