Adjustable Load Capacity Forecasting Technology Based on Unsupervised Learning

In the existing power grid demand management, there is a lack of perfect demand side resource load response analysis and load schedulable capacity analysis. Aiming at the problem of load adjustable capacity in the power grid, an adjustable load adjustment capacity evaluation model is established to convert the load into load characteristic parameters, and the fuzzy c-means clustering algorithm based on peak density is used to process the load characteristic parameters to accurately identify the adjustable load; Aiming at several influencing factors of adjustable load, the adjustable capacity of adjustable load is explored by using multi-core function, and the capacity is evaluated by different indexes.