Spatial Load Prediction of Urban Distribution Grid under the Low-carbon Concept. Issue 1 (1st March 2022)

• Record Type: Journal Article
• Authors: Zheng, Zhen; Jiang, Chen; Zhang, Guanhua; Han, Wanli; Liu, Jiale
• Is Part Of: Journal of physics. Volume 2237:Issue 1(2022)
• Publication Date: 2022-03-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2237/1/012008
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Under the goal of "dual carbon", distribution grid is the basic platform serving the country to achieve carbon peak and carbon neutral goals, and spatial load prediction is of great significance to the planning and construction of distribution grid. To improve the accuracy of the spatial load prediction of the distribution grid, this paper proposes an algorithm based on the ELM (Extreme Learning Machine), using the parameters of the particle group optimization model. Through the simulation experiment of the searched data, the relative error of the FCM algorithm, the PSO optimization algorithm has high accuracy and meets the requirements of practical engineering.