AI-Based Predictive Analytics Integrated with Transportation Optimization for Solar-Panel Distribution

Abstract

Solar- photovoltaic installations have been growing very fast such that, distribution of solar panels has become complex because of unpredictable demand, unpredictable transport costs, and the inability to schedule installations. The classical models of transportation optimization reduce distribution cost on a deterministic basis and thus fail to work in dynamic and policy-constituted renewable-energy set-ups. This paper suggests a hybrid approach, which is the combination of AI-based predictive analytics and transportation optimization to enhance the effectiveness and responsiveness of solar-panel distribution nets. Random Forest, Gradient Boosting, and LSTM networks are machine-learning models that predict the demand of solar-panels in each region according to 24 months of historical and secondary data. The projected values of demand are coded as dynamic inputs into a linear-programming transportation model which is employed to minimize the total transportation and installation expenses. Numerical experiment: The proposed AI-integrated model is compared to a traditional fixed in place transportation model in terms of multiple supply centres and installation areas. The findings indicate that the AI-assisted system saves a total distribution cost by 12-15 percent, better installation scheduling, and reduced emergency shipment due to demand uncertainty by a significant margin. The research is significant enough to add to the renewable-energy logistics literature showing how predictive intelligence may be operationally integrated into classical optimization. The suggested model offers an effective decision support system to solar-energy companies and governments that are interested in more affordable, scalable, and resilient distribution planning.