Application of Trajectory Clustering on Improved Artificial Intelligence Load Forecasting

Capacity of grid participants such as photovoltaic panel, wind power and electric vehicles has shown rapid growth, while the technology in the industrial sector has continued to innovate, and many types of power load characteristics have undergone tremendous changes. This paper proposes an improved worker intelligence load forecasting method based on trajectory clustering. The method clusters different fluctuation patterns of daily load curves, summarizes the influencing factors, and improves the intelligent prediction method. It is suitable for load forecasting in areas where load characteristics are not significantly consistent and where external stimuli are significantly affected. Solve the distortion problem of traditional load forecasting for continuous prediction of new energy and random power load. The calculations are carried out for areas with industrial agglomeration and new energy capacity. The results show that the method can accurately reflect the future load trend from the perspective of feature correction.