Exploration of AI fusion technology for predicting new energy network investment in response to regional load changes

Due to the uncertainty and volatility of regional load changes, the investment demand for new energy access systems tends to increase, and existing forecasting and decision-making methods have limitations in response speed and accuracy. This article proposes a response plan based on intelligent integration of regional load change characteristics, seeking a new strategy for predicting new energy investment. By constructing a series of multi-dimensional fusion data-driven load change prediction models to characterize the temporal and spatial changes, we aim to enhance the dynamic perception and response capabilities of regional loads; Based on the predicted results, design an investment decision optimization mechanism for intelligent management of regional renewable energy layout; At the same time, a dynamic adaptation mechanism is introduced to enable this model to automatically optimize and improve based on the load characteristics of different regions; Research and establish an experimental platform, conduct training and evaluation using typical regional load data, and comprehensively analyze the prediction accuracy, investment return prediction, and dynamic decision support effect. The results show that it performs well in tracking load changes, estimating investment returns, and providing dynamic decision support, providing effective support for new energy access planning and regional power system optimization.