Substation Load Characteristics and Forecasting Model for Large-scale Distributed Generation Integration. Issue 3 (March 2020)

• Record Type: Journal Article
• Authors: Gao, Zhengping; Shi, Jing; Li, Hu; Chen, Chen; Tan, Jian; Liu, Lixin
• Is Part Of: IOP conference series. Volume 782:Issue 3(2020)
• Publication Date: 2020-03
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/782/3/032044
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The large-scale access of distributed generation has a great impact on load forecasting in substation-area, which means the load curve in the substation-area cannot reflect the real load of users. Firstly, this paper considers the impact of distributed generation on the load curve, and proposes a load forecasting model based on data cleaning and deep learning in the substation-area. Secondly, considering the data missing during communication and transmission, the KNN algorithm is adopted to complete the missing data before the data input. And then, Pearson correlation coefficients are used for the correlation analysis of the input factors related to distributed generation, and the data is trained through Long-short Term Memory in deep learning. Finally, verified by load data of some substation-area in Jiangsu Province, the prediction model established in this paper has good prediction accuracy and stability.