Ultra-Short-Term Photovoltaic Power Prediction Based on Predictable Component Reconstruction and Spatiotemporal Heterogeneous Graph Neural Networks
Ultra-short-term PV power prediction (USTPVPP) results provide a basis for the development of intra-day rolling power generation plans. However, due to the feature information and the unpredictability of meteorology, the current ultra-short-term PV power prediction accuracy improvement still faces t...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-08-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/15/4192 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849239752092418048 |
|---|---|
| author | Yingjie Liu Mao Yang |
| author_facet | Yingjie Liu Mao Yang |
| author_sort | Yingjie Liu |
| collection | DOAJ |
| description | Ultra-short-term PV power prediction (USTPVPP) results provide a basis for the development of intra-day rolling power generation plans. However, due to the feature information and the unpredictability of meteorology, the current ultra-short-term PV power prediction accuracy improvement still faces technical challenges. In this paper, we propose a combined prediction framework that takes into account the reconfiguration of the predictable components of PV stations and the spatiotemporal heterogeneous maps. A circuit singular spectral decomposition (CISSD) intrinsic predictable component extraction method is adopted to obtain specific frequency components in sensitive meteorological variables, a mechanism based on radiation characteristics and PV power trend predictable component extraction and reconstruction is proposed to enhance power predictability, and a spatiotemporal heterogeneous graph neural network (STHGNN) combined with a Non-stationary Transformer (Ns-Transformer) combination architecture to achieve joint prediction for different PV components. The proposed method is applied to a PV power plant in Gansu, China, and the results show that the prediction method based on the proposed combined spatio-temporal heterogeneous graph neural network model combined with the proposed predictable component extraction achieves an average reduction of 6.50% in the RMSE, an average reduction of 2.50% in the MAE, and an average improvement of 11.93% in the R<sup>2</sup> over the direct prediction method, respectively. |
| format | Article |
| id | doaj-art-b7a309e98d1d4303a3d9c552e4562686 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-b7a309e98d1d4303a3d9c552e45626862025-08-20T04:00:50ZengMDPI AGEnergies1996-10732025-08-011815419210.3390/en18154192Ultra-Short-Term Photovoltaic Power Prediction Based on Predictable Component Reconstruction and Spatiotemporal Heterogeneous Graph Neural NetworksYingjie Liu0Mao Yang1Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, ChinaKey Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, ChinaUltra-short-term PV power prediction (USTPVPP) results provide a basis for the development of intra-day rolling power generation plans. However, due to the feature information and the unpredictability of meteorology, the current ultra-short-term PV power prediction accuracy improvement still faces technical challenges. In this paper, we propose a combined prediction framework that takes into account the reconfiguration of the predictable components of PV stations and the spatiotemporal heterogeneous maps. A circuit singular spectral decomposition (CISSD) intrinsic predictable component extraction method is adopted to obtain specific frequency components in sensitive meteorological variables, a mechanism based on radiation characteristics and PV power trend predictable component extraction and reconstruction is proposed to enhance power predictability, and a spatiotemporal heterogeneous graph neural network (STHGNN) combined with a Non-stationary Transformer (Ns-Transformer) combination architecture to achieve joint prediction for different PV components. The proposed method is applied to a PV power plant in Gansu, China, and the results show that the prediction method based on the proposed combined spatio-temporal heterogeneous graph neural network model combined with the proposed predictable component extraction achieves an average reduction of 6.50% in the RMSE, an average reduction of 2.50% in the MAE, and an average improvement of 11.93% in the R<sup>2</sup> over the direct prediction method, respectively.https://www.mdpi.com/1996-1073/18/15/4192circulant singular spectrum decompositionpredictable component extraction and reconstructionSTHGNN-Ns-Transformerultra-short-term photovoltaic power prediction |
| spellingShingle | Yingjie Liu Mao Yang Ultra-Short-Term Photovoltaic Power Prediction Based on Predictable Component Reconstruction and Spatiotemporal Heterogeneous Graph Neural Networks Energies circulant singular spectrum decomposition predictable component extraction and reconstruction STHGNN-Ns-Transformer ultra-short-term photovoltaic power prediction |
| title | Ultra-Short-Term Photovoltaic Power Prediction Based on Predictable Component Reconstruction and Spatiotemporal Heterogeneous Graph Neural Networks |
| title_full | Ultra-Short-Term Photovoltaic Power Prediction Based on Predictable Component Reconstruction and Spatiotemporal Heterogeneous Graph Neural Networks |
| title_fullStr | Ultra-Short-Term Photovoltaic Power Prediction Based on Predictable Component Reconstruction and Spatiotemporal Heterogeneous Graph Neural Networks |
| title_full_unstemmed | Ultra-Short-Term Photovoltaic Power Prediction Based on Predictable Component Reconstruction and Spatiotemporal Heterogeneous Graph Neural Networks |
| title_short | Ultra-Short-Term Photovoltaic Power Prediction Based on Predictable Component Reconstruction and Spatiotemporal Heterogeneous Graph Neural Networks |
| title_sort | ultra short term photovoltaic power prediction based on predictable component reconstruction and spatiotemporal heterogeneous graph neural networks |
| topic | circulant singular spectrum decomposition predictable component extraction and reconstruction STHGNN-Ns-Transformer ultra-short-term photovoltaic power prediction |
| url | https://www.mdpi.com/1996-1073/18/15/4192 |
| work_keys_str_mv | AT yingjieliu ultrashorttermphotovoltaicpowerpredictionbasedonpredictablecomponentreconstructionandspatiotemporalheterogeneousgraphneuralnetworks AT maoyang ultrashorttermphotovoltaicpowerpredictionbasedonpredictablecomponentreconstructionandspatiotemporalheterogeneousgraphneuralnetworks |