امیررضا صادقی کیادهی

Objective : Off-grid renewable energy systems are essential for ensuring a sustainable and reliable electricity supply in regions where access to the conventional grid is limited or economically unjustifiable. Designing such systems requires systematic and efficient methodologies that can guide planners in selecting appropriate technologies under diverse environmental and consumption conditions. This study aims to develop an intelligent sizing framework for hybrid renewable energy systems by integrating expert knowledge with geographical, climatic, and load-related variables. The proposed model seeks to identify the optimal combination of photovoltaic panels, wind turbines, battery storage systems, and diesel generators for off-grid applications in Iran.  Methodology: The research methodology employs a two-stage approach. In the first stage, key variables influencing system sizing were identified through a comprehensive review of prior studies and structured interviews with academic and industrial experts. These interviews provided valuable operational insights, enabling the determination of qualitative ranges for model inputs and outputs. In the second stage, the collected expert knowledge was translated into fuzzy rules and implemented within a fuzzy inference system developed in MATLAB, forming an intelligent decision-support engine capable of evaluating multiple operational scenarios. Results : The findings indicate that the proposed model accurately determines optimal configurations for various geographical locations and consumption profiles. Model outputs showed less than 10% deviation from 80% of expert assessments. Moreover, the model generates sizing recommendations within minutes, significantly improving the speed of decision-making. Conclusion : In summary, the developed framework provides a practical and efficient tool for planners and stakeholders involved in designing off-grid hybrid renewable energy systems.