Forecasting and gap analysis of renewable energy integration in zero energy-carbon buildings: a comprehensive bibliometric and machine learning approach

Abstract

This paper investigates biomass and solar energy's present and future perspectives in low/zero energy and carbon emissions. Its data source is published articles indexed in the Scopus database. By analyzing the articles extracted in Vos viewer software, four main areas of research are found: sustainable development, economic and managerial issues, methods, algorithms, modeling technologies, and renewable energy and its sources and types. In all four sections, research gaps were observed in the field of the third generation of photovoltaics (semi-transparent solar cells )organic)) and algae. As part of the study, advanced bibliometric analysis was carried out by VOS viewer software, and 34129 articles were examined from Scopus, alongside a patent analysis using Google patents, in addition to the bibliometric analysis. It has been shown by machine learning that about 9% of future articles in all energy fields will consist of building articles, and a quarter of these articles will be in the field of renewable energy. While residential and commercial sectors are the dominant areas of renewable energy utilization and commercialization research, the potential of new generations of renewable energy technologies will create significant opportunities to achieve low/zero energy-carbon emission buildings. The paper concludes by predicting the increasing rate of renewable energy and building articles compared to energy articles by 2030, emphasizing the critical role of research in advancing sustainable energy solutions. This data mining analysis helps to identify the current gaps and opportunities. Therefore, great potential will be created to develop and commercialize a new generation of technologies in this industry.

Keywords: Bibliometric; Building; Data mining; Machine learning; Microalgae; Semi-transparent solar cells; Sustainability.