Energy Prediction of Edge-side Photovoltaic Power Generation Based on Hierarchical Fuzzy Neural Network

The wide access of new energy, represented by distributed photovoltaic, has brought about new demands such as real-time business responsiveness intelligent analysis and processing within the substations. The State Grid Corporation currently lacks a solution to address the issue of local autonomy of distributed new energy consumption. The key to optimizing new energy consumption lies in accurate prediction of photovoltaic power generation and rapid development of edge devices for Internet of Things. Based on the power grid dispatching Internet of Things, in this paper we propose a local autonomy solution for new energy consumption combined with the energy router of the substation. An energy controller is designed for distributed new energy consumption, and the energy produced by photovoltaic power generation is predicted based on hierarchical fuzzy neural network algorithm. The research results demonstrate that the energy controller developed in this paper, along with the hierarchical fuzzy neural network model, exhibit certain effects and advantages in enhancing the autonomy at edge-side stations.