Two-stage photovoltaic power forecasting method with an optimized transformer

Yanhong Ma; Feng Li; Hong Zhang; Guoli Fu; Min Yi

doi:10.1016/j.gloei.2024.11.011

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Two-stage photovoltaic power forecasting method with an optimized transformer

Clean Energy Development | 更新时间：2026-09-20

- Two-stage photovoltaic power forecasting method with an optimized transformer
- Global Energy Interconnection Vol. 7, Issue 6, Pages: 812-824(2024)
- 作者机构：
  
  1. State Grid Gansu Electric Power Company,Lanzhou,PR China,730000
  2. School of Electrical & Information Engineering, Lanzhou University of Technology,Lanzhou,PR China,730050
  3. School of Computer & Communication, Lanzhou University of Technology,Lanzhou,PR China,730050
- 作者简介：
- 基金信息：
- DOI：10.1016/j.gloei.2024.11.011
  CLC：
- Online First：16 December 2024，
  
  Published：16 December 2024
- 稿件说明：
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Yanhong Ma, Feng Li, Hong Zhang, et al. Two-stage photovoltaic power forecasting method with an optimized transformer[J]. Global Energy Interconnection, 2024, 7(6): 812-824.
DOI：

Yanhong Ma, Feng Li, Hong Zhang, et al. Two-stage photovoltaic power forecasting method with an optimized transformer[J]. Global Energy Interconnection, 2024, 7(6): 812-824. DOI： 10.1016/j.gloei.2024.11.011.

摘要

Abstract

urate photovoltaic (PV) power forecasting ensures the stability and reliability of power systems. To address the complex characteristics of nonlinearity

volatility

and periodicity

a novel two-stage PV forecasting method based on an optimized transformer architecture is proposed. In the first stage

an inverted transformer backbone was utilized to consider the multivariate correlation of the PV power series and capture its non-linearity and volatility. ProbSparse attention was introduced to reduce high-memory occupation and solve computational overload issues. In the second stage

a weighted series decomposition module was proposed to extract the periodicity of the PV power series

and the final forecasting results were obtained through additive reconstruction. Experiments on two public datasets showed that the proposed forecasting method has high accuracy

robustness

and computational efficiency. Its RMSE improved by 31.23% compared with that of a traditional transformer

and its MSE improved by 12.57% compared with that of a baseline model.

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