Tech Mining Analysis: Renewable Energy Forecasting Using Artificial Intelligence Technologies

Abstract

Extracting useable information from large and complex data is one of the challenges that can be solved by data mining. The use of data mining to understand the state of research fields and the underlying technologies which are related in intricate ways is known as bibliometric analysis or "tech mining". Tech mining can help in summarizing the important research results that are valuable to researchers, industry, and decision-making. Furthermore, tech mining can be useful in showing the trends and relations of the analyzed topic. Hence, the purpose of this research is to investigate the status and the evolution of scientific studies in the field of renewable energy forecasting using Artificial Intelligence (AI) technologies. The study seeks to address the status of the scientific production indexed in Scopus based on scientometrics indicators. In total, over 25000 articles were extracted. The collected data were analyzed using R software packages. The findings show the exponential growth of this topic in the past 10 years. The most relevant sources, authors, affiliations, and countries were found. Moreover, the most recent trends and technologies were stated. It was also found that the interest in this research topic is prevalent in Asian countries especially China which has the highest number of publications and citations. Moreover, it was found that the most fund for research in this domain comes from Chinese institutions.