Big data adoption in smart renewable energy networks toward sustainability through decision-making methods under uncertainty

• Using big data in renewable energy (RE) to gain energy balances and sustainability • Presenting a decision-making framework for assessing big data challenges in RE • Considering fuzzy theory in the presented method to handle ambiguity in judgments • Proposing sensitivity analysis and comparative analysis of decision-making methods • Introducing a new application to use big data to improve the RE industry in Iran Renewable Energy (RE) is crucial for fulfilling global energy demands and lowering pollution, and big data can optimize RE management by improving demand forecasting and uncovering consumption trends. However, research on its integration into RE is limited, and the challenges to adoption need further investigation. To fill the gaps, this examination provides a decision-making approach for adopting big data into RE development. Big data implementation challenges are prioritized using decision-making techniques, and to address uncertainties, fuzzy theory and hybrid approaches of the Fuzzy Simplified Best-Worst Method (F-SBWM) and Fuzzy Combined Compromise Solution (F-CoCoSo) are used to assess the challenges. Also, validation and sensitivity analysis will be examined. The adequate rate of return on investment, availability of financial capital and resources, and technical infrastructure have the highest importance with values of 0.238, 0.149, and 0.147, respectively. Also, the findings indicate that the challenges of high costs, the uncertainty of financing, and lack of financial support for the project, and privacy and cyberattack risks/security issues are the most priority in the assessments, respectively. Decision-makers should manage costs and secure financing through partnerships, green loans, and tax breaks. Establishing privacy and cybersecurity policies is vital for trust, while leveraging new technologies and transparent legal frameworks can mitigate cyber risks.