The selection of suitable renewable energy infrastructure is a critical challenge in sustainable power planning due to the variability of climatic conditions and regional geographical characteristics. Conventional machine learning approaches primarily rely on numerical meteorological parameters, often ignoring contextual environmental knowledge that influences practical deployment feasibility. In this work, a hybrid decision support framework is suggested, which combines the databased prediction with knowledge-based frameworks. The suggested system employs climatic parameters derived by satellites such as the solar irradiance, temperature, humidity, precipitation, and wind speed as NASA POWER datasets, to be trained on a supervised machine preliminary energy suitability classification learning model between solar, wind, hydro and biomass sources. The contextual reasoning module converts textual information to solve the weakness of statistical prediction alone environmental descriptions that describe geographic features like mountainous geography, coastal winds, etc. and agricultural activity. Decision fusion mechanism involves the combination of machine learning prediction with contextual inference to give a decision refined recommendation.The analysis of experiments in various climatic regions of India proves that the hybrid framework is more efficient accuracy of the decision over single machine learning prediction, especially in geographically sensitive areas such as coastal zones. The offered method resembles the behavior of an expert planner, where quantitative climate analysis is integrated with. qualitative environmental knowledge, therefore, giving a more practical and explicable renewable energy planning tool. The system would be able to guide planners, researchers, and policy makers on the appropriate renewable energy better contextual awareness installations
Sowmya P R (Thu,) studied this question.