Smart Grids Data Analysis: A Systematic Mapping Study

TL;DR

This systematic mapping study reviews the diverse data analysis approaches in Smart Grids, highlighting research trends, techniques, and challenges such as limited replicability and the importance of simulations.

Contribution

It provides a comprehensive overview of current research facets, methodologies, and tools used in Smart Grid data analysis, identifying gaps and future directions.

Findings

Different sub-domains have unique techniques and approaches.

Simulations and experiments are widely used in research.

Replicability of studies is limited due to private datasets and lack of implementation details.

Abstract

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.