Data stream fusion for predicting electricity priceKonda, Jaka (Avtor) Bosnić, Zoran (Mentor) Legenstein, Robert (Komentor) machine learningtime-series forecastingincremental learningdata fusiondata streamingelectricity price predictionIn this work, we tackle the problem of building a predictive model for the electricity market. In it, market players compete for the best prices to increase their profits and now with transition to renewable energy sources, the market has become more volatile and dependant on different environmental factors. In our experiments we use different statistical and machine learning methods in a combination with data sources from European energy platform and European meteorological institute to obtain weather information. We fuse three different data sources together into four different datasets. We follow the machine learning pipeline where we select features, model hyper-parameters and test the models on the test set. Contrary to our expectations, additional weather information had a negative impact on the error and the variance of the models. Some improvements in the prediction accuracy were noted only when we included additional datasets with most important selected features being the lagged values of the target variable and also past and forecasted energy load. Our best method is obtained using late level fusion by combining all trained regressors together into an ensemble. It achieves an sMAPE error of 13.084, compared to our second best, neural network, with an error of 14.932 and baseline with the value of 22.963.20192019-12-19 14:40:01Magistrsko delo/naloga113304sl