EFFECTS OF ARTIFICIAL INTEGRATION AND BIG DATA ANALYSIS ON ECONOMIC VIABILITY OF SOLAR MICROGRIDS: MEDIATING ROLE OF COST BENEFIT ANALYSIS

Abstract

The escalating environmental alterations have precipitated a transition towards renewable energy sources. Concurrently, recent technological advancements, such as big data and artificial intelligence, have fundamentally altered the landscape of decision-making processes. Consequently, this research endeavors to assess the impact of integrating big data and AI on the economic feasibility of solar microgrids in the rural context of Jordan. Furthermore, the study investigates several mediating factors, including adoption rates, costs, economic benefits, decision-making processes, investments, and community acceptance. The data were gathered from 250 professionals involved in solar microgrid initiatives, utilizing a quantitative deductive approach and employing questionnaires as the primary data collection tool. Subsequently, the collected data underwent statistical analysis through SPSS and PLS-SEM. The findings of the study indicate a positive and significant influence of big data and AI integration on adoption rates, costs, economic benefits, decision-making processes, investments, and community acceptance. Moreover, the results suggest that economic benefits are influenced by adoption rates, costs, economic benefits, decision-making processes, investments, and community acceptance. Importantly, all identified mediation paths were found to be statistically significant. These outcomes underscore the potential of big data and AI integration to enhance decision-making processes in favour of adopting solar microgrids by providing crucial insights into their benefits. The study concludes with a discussion on research limitations and outlines potential directions for future investigations.

Keywords

• Solar Microgrids
• Artificial Intelligence
• Big Data Analytics
• Economic Viability
• Renewable Energy
• Community Acceptance