  Undoubtedly, the increase of available data and competitive machine learning
algorithms has boosted the popularity of data-driven modeling in energy
systems. Applications are forecasts for renewable energy generation and energy
consumption. Forecasts are elementary for sector coupling, where
energy-consuming sectors are interconnected with the power-generating sector to
address electricity storage challenges by adding flexibility to the power
system. However, the large-scale application of machine learning methods in
energy systems is impaired by the need for expert knowledge, which covers
machine learning expertise and a profound understanding of the application's
process. The process knowledge is required for the problem formalization, as
well as the model validation and application. The machine learning skills
include the processing steps of i) data pre-processing, ii) feature
engineering, extraction, and selection, iii) algorithm selection, iv)
hyperparameter optimization, and possibly v) post-processing of the model's
output. Tailoring a model for a particular application requires selecting the
data, designing various candidate models and organizing the data flow between
the processing steps, selecting the most suitable model, and monitoring the
model during operation - an iterative and time-consuming procedure. Automated
design and operation of machine learning aim to reduce the human effort to
address the increasing demand for data-driven models. We define five levels of
automation for forecasting in alignment with the SAE standard for autonomous
vehicles, where manual design and application reflect Automation level 0.
