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.

Wind power and other forms of renewable energy sources play an ever more important role in the energy supply of today's power grids. Forecasting renewable energy sources has therefore become essential in balancing the power grid. While a lot of focus is placed on new forecasting methods, little attention is given on how to compare, reproduce and transfer the methods to other use cases and data. One reason for this lack of attention is the limited availability of open‐source datasets, as many currently used datasets are non‐disclosed and make reproducibility of research impossible. This unavailability of open‐source datasets is especially prevalent in commercially interesting fields such as wind power forecasting. However, with this paper, we want to enable researchers to compare their methods on publicly available datasets by providing the, to our knowledge, largest up‐to‐date overview of existing open‐source wind power datasets, and a categorization into different groups of datasets that can be used for wind power forecasting. We show that there are publicly available datasets sufficient for wind power forecasting tasks and discuss the different data groups properties to enable researchers to choose appropriate open‐source datasets and compare their methods on them.

A hybrid Energy Storage Systems (ESS) consists of two or more energy storage technologies, with different power and energy characteristics. Using a hybrid ESS, both high-frequency and low-frequency power variations can be addressed at the same time. For an accurate sizing of a hybrid ESS, the use of high-resolution data is required. However, high-resolution data over long periods leads to large data sets, which are difficult to handle. In this paper, an improved motif discovery algorithm is introduced to find the most recurring daily consumption patterns within the time series of interest. The most recurring pattern is selected as the representative of the time series for sizing the hybrid ESS. Next, a simple optimization framework is proposed for selecting the cut-off frequency of a low-pass filter, used for allocating the power to different storage technologies. Finally, the proposed sizing approach is applied for sizing a hybrid battery-flywheel ESS at four different low voltage distribution grids in southern Germany using real measurement data. It is demonstrated that a hybrid ESS, with the characteristics derived from the most recurring patterns only, can effectively provide their intended grid services for most of the days during the whole period of the time series.

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biassed and underdispersed, meteorologists post‐process the ensembles. This post‐processing can successfully correct the biasses in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post‐processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post‐processing method and evaluate four possible strategies: only using the raw ensembles without post‐processing, a one‐step strategy where only the weather ensembles are post‐processed, a one‐step strategy where we only post‐process the power ensembles and a two‐step strategy where we post‐process both the weather and power ensembles. Results show that post‐processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness whilst only post‐processing the weather ensembles does not necessarily lead to increased forecast performance.