• Probabilistic Load Forecasting with Deep Conformalized Quantile Regression 

      Jensen, Vilde (Master thesis; Mastergradsoppgave, 2021-06-01)
      The establishment of smart grids and the introduction of distributed generation posed new challenges in energy analytics that can be tackled with machine learning algorithms. The latter, are able to handle a combination of weather and consumption data, grid measurements, and their historical records to compute inference and make predictions. An accurate energy load forecasting is essential to assure ...