Towards predictive modelling of solar power productionHandledare: Hadi BanaeeExaminer: Andrey Kiselev© Hadi

Abstract: In 2019, 732 solar panels were installed on the roof of a building at Örebro University. Thesolar power production of the facility has been collected in a database in Akademiska Hus,along with several parameters from a weather station in the same building. The goal of thisproject is to model solar power production as a function of weather parameters and historicalvalues using machine learning techniques. This study investigates various predictive models tofind a suitable model for predicting this production. There have been several studies in theliterature that have performed this goal in various laboratory environments and other places,but not for this facility. The measured data for this study is recorded by Akademiska Hus forover two years from 2019 to 2021. The results of this work lead to two suitable machine learningmodels while using weather parameters: 1) Narrow Neural Network and 2) Support VectorMachine with 7% errors in both models. Moreover, this study has investigated univariatemodels to predict the solar power production as a time series based on its historical data. Forthis aim, a Nonlinear Autoregressive Neural Network has been applied which results inconsiderably low errors in the evaluations.