Insights Into Energy Indicators Analytics Towards European Green Energy Transition Using Statistics and Self-Organizing Maps

Abstract

The more frequent meteorological anomalies and climate changes push us to consider green sustainable energy as a chance to slow down such issues. Thus, we should introspect the correlations between indicators over time and understand the underneath of their meaning. Large volumes of data regarding energy are provided by Eurostat and other official data sources that require data analytics to extract valuable insights from energy indicators and indices to better understand the dynamics towards a green energy transition of the European Union State Members (EU-SM). In this paper, we analyze several energy indicators calculated for a 12-year time span with statistics and machine learning techniques, such as an unsupervised clustering algorithm with Self-Organizing Maps (SOM). Grouping the EU-SM by energy indicators from the beginning years to the end of the analyzed interval reveals differences and similarities in their efforts, shifted trends, influencing power and tendencies towards a green energy transition. The results of our analyses can be further used to assess the efficiency of stimuli for green energy generation and improve the policymakers’ strategies.

Keywords

• Artificial neural networks
• correlation
• energy management
• renewable energy sources
• self-organizing feature maps
• statistics