The Path to a Sustainable Energy Europe: An Analysis of the Transition from Fossil Fuels to Renewable Energy

Abstract

The main purpose of this study is to investigate the application of predictive analytics techniques in the energy field and to identify strategies to stimulate renewable energy production. The analysis begins by examining four key indicators: renewable energy, gas price, gas consumption and renewable energy consumption through a detailed descriptive analysis. Visual graphs are built to get an overview of the evolution of the energy sector in the period 2011-2020 with the help of Tableau software. The analysis is supported by the use of the Random Forest algorithm as a prediction model, considering critical indicators such as gas price, gas consumption and renewable energy consumption. The prediction results provide insight into anticipating changes in renewable energy production in the European countries studied. At the same time, the study highlights the current situation of renewable energy in Europe and identifies the necessary measures for its sustainable development through the analysis of specialized literature. It also examines the way in which big data management, facilitated by technologies such as smart meters and drone sensors, can help improve the energy sector. This research offers valuable results, providing insights into the evolution of renewable and fossil energy, as well as a detailed comparison of renewable energy, gas prices, gas consumption and trends in renewable energy consumption. By integrating predictive analytics techniques, data management and renewable energyspecific indicators, this study makes an innovative contribution to energy systems analysis. Its focus on European countries contributes to the understanding of the growth potential of renewable energy generation in this region.

Keywords

• sustainability
• renewable energy
• energy consumption
• fossil fuels
• random forest
• predictive analysis