Intelligent Site Selection of Distributed Photovoltaic Power Stations Based on a Multi-site Forecasting Model

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Intelligent Site Selection of Distributed Photovoltaic Power Stations Based on a Multi-site Forecasting Model

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[1]SONG Ling,CHANG Longtao,LYU Shunming,et al.Intelligent Site Selection of Distributed Photovoltaic Power Stations Based on a Multi-site Forecasting Model[J].Journal of Zhengzhou University (Engineering Science),2025,46(02):119-126.[doi:10.13705/j.issn.1671-6833.2025.02.016]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 02 Page number: 119-126 Column: Public date: 2025-03-10

Title:: Intelligent Site Selection of Distributed Photovoltaic Power Stations Based on a Multi-site Forecasting Model

Author(s):: SONG Ling¹; CHANG Longtao¹; LYU Shunming²; YANG Zhaohui¹; LIU Xinfeng¹; CHEN Guanzhong¹; 1.College of Computer Science and Technology, ShanDong JianZhu University, Jinan 250101,China;2.Cloud Operations Center, State Grid Information & Telecommunication Branch, Beijing 100000, China

Keywords:: intelligent site selection; multi-site power output forecasting; deep residual networks; model fusion; spatiotemporal correlation

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2025.02.016

Abstract:: In order to improve the operational efficiency of photovoltaic (PV) power stations a proposal of multi-site forecasting model (MSFM) was proposed to addressing the multi-site location selection problem. In the proposed model, spatiotemporal correlation, event data, and meteorological factors were leveraged to predict power output across multiple sites. A three-dimensional tensor was introduced to represent spatiotemporal data, and tensor decomposition techniques were utilized to recover missing entries. Additionally, the spatiotemporal adjacency, trends, event text data, and meteorological impacts were modeled using both the three-dimensional tensor and the ResNet model. An experimental dataset was established using operational and meteorological data from 1,155 PV power stations in Jinan, Shandong Province. The performance of the proposed method was validated through mean absolute error, relative absolute error, root mean square error, and relative root mean square error, with at least 2.3%, 0.9%, 2.6%, and 2.5% reductions, respectively, in these four evaluation metrics, the experimental results demonstrated that the proposed method was applicable to multi-site location selection problems.

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Last Update: 2025-03-13