  Data analytics and data science play a significant role in nowadays society.
In the context of Smart Grids (SG), the collection of vast amounts of data has
seen the emergence of a plethora of data analysis approaches. In this paper, we
conduct a Systematic Mapping Study (SMS) aimed at getting insights about
different facets of SG data analysis: application sub-domains (e.g., power load
control), aspects covered (e.g., forecasting), used techniques (e.g.,
clustering), tool-support, research methods (e.g., experiments/simulations),
replicability/reproducibility of research. The final goal is to provide a view
of the current status of research. Overall, we found that each sub-domain has
its peculiarities in terms of techniques, approaches and research methodologies
applied. Simulations and experiments play a crucial role in many areas. The
replicability of studies is limited concerning the provided implemented
algorithms, and to a lower extent due to the usage of private datasets.
