  Purpose: Trading on electricity markets occurs such that the price settlement
takes place before delivery, often day-ahead. In practice, these prices are
highly volatile as they largely depend upon a range of variables such as
electricity demand and the feed-in from renewable energy sources. Hence,
accurate forecasts are demanded.
  Approach: This paper aims at comparing different predictors stemming from
supply-side (solar and wind power generation), demand-side, fuel-related and
economic influences. For this reason, we implement a broad range of non-linear
models from machine learning and draw upon the information-fusion-based
sensitivity analysis.
  Findings: We disentangle the respective relevance of each predictor. We show
that external predictors altogether decrease root mean squared errors by up to
21.96%. A Diebold-Mariano test statistically proves that the forecasting
accuracy of the proposed machine learning models is superior.
  Originality: The benefit of adding further predictors has only recently
received traction; however, little is known about how the individual variables
contribute to improving forecasts in machine learning.

Applications