Successful companies are increasingly those companies that excel in the task of extracting knowledge from data. Tapping the source of 'Big Data' requires powerful algorithms combined with a strong understanding of the data used. One of the key challenges in predictive analytics is the identification of relevant factors that may explain the variables of interest. In this paper, we present a case study in predictive analytics in which we focus on the selection of relevant exogenous variables. More specifically, we attempt to predict the German electricity spot prices with reference to historical prices and a deep set of weather variables. In order to choose the relevant weather stations, we use the least absolute shrinkage selection operation (LASSO) and random forests to implicitly execute a variable selection. Overall, in our case study of German weather data, we manage to improve forecasting accuracy by up to 16.9% in terms of mean average error.

The increasing share of renewable energy generation in the electricity system comes with significant challenges, such as the volatility of renewable energy sources. To tackle those challenges, demand side management is a frequently mentioned remedy. However, measures of demand side management need a high level of flexibility to be successful. Although extensive research exists that describes, models and optimises various processes with flexible electrical demands, there is no unified notation. Additionally, most descriptions are very process-specific and cannot be generalised.In this paper, we develop a comprehensive modelling framework to mathematically describe demand side flexibility in smart grids while integrating a majority of constraints from different existing models. We provide a universally applicable modelling framework for demand side flexibility and evaluate its practicality by looking at how well Mixed-Integer Linear Program (MIP) solvers are able to optimise the resulting models, if applied to artificially generated instances. From the evaluation, we derive that our model improves the performance of previous models while integrating additional flexibility characteristics.

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