A Photovoltaic Power Prediction Model Integrating Multi-source Heterogeneous Meteorological Data

TAN Ling; KANG Ruixing; XIA Jingming; WANG Yue

doi:10.11999/JEIT230731

Volume 46 Issue 2

Feb. 2024

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TAN Ling, KANG Ruixing, XIA Jingming, WANG Yue. A Photovoltaic Power Prediction Model Integrating Multi-source Heterogeneous Meteorological Data[J]. Journal of Electronics & Information Technology, 2024, 46(2): 503-517. doi: 10.11999/JEIT230731

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TAN Ling, KANG Ruixing, XIA Jingming, WANG Yue. A Photovoltaic Power Prediction Model Integrating Multi-source Heterogeneous Meteorological Data[J]. Journal of Electronics & Information Technology, 2024, 46(2): 503-517. doi: 10.11999/JEIT230731

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A Photovoltaic Power Prediction Model Integrating Multi-source Heterogeneous Meteorological Data

doi: 10.11999/JEIT230731

1.
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
3.
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China

Received Date: 2023-07-19
Rev Recd Date: 2023-09-28

Available Online: 2023-10-10

Publish Date: 2024-02-29

Abstract

Abstract

High-precision photovoltaic power prediction is of great significance for improving the operation efficiency of power system. Photovoltaic power is affected by many factors, among which cloud change is the most important uncertain factor. However, the traditional photovoltaic power prediction methods do not fully consider the influence of cloud three-dimensional structure and meteorological factors on photovoltaic power. To solve this problem, a Multi-source variables Photovoltaic power Prediction Model (MPPM) based on integrating multi-source heterogeneous meteorological data is proposed. The core of MPPM includes SpatioTemporal feature Conditional Diffusion Model (STCDM), Attention Stacked LSTM network (ASLSTM) and Multidimensional Feature Fusion Module (MFFM). STCDM accurately predicts the two-dimensional satellite cloud image, eliminating the blurring phenomenon at the cloud boundary. ASLSTM extracts the three-dimensional Weather Research and Forecasting model (WRF) meteorological element features. MFFM fuses the two-dimensional satellite cloud image features and three-dimensional WRF meteorological element features to obtain the photovoltaic power prediction results for the next 1 h. In this paper, satellite cloud image prediction experiment and photovoltaic power prediction experiment are carried out by using STCDM model and MPPM model respectively. The results show that the Structural SIMilarity index (SSIM) of STCDM in satellite cloud image prediction within 1 h is up to 0.914, and the CORRelation index (CORR) of MPPM in photovoltaic power prediction within 1 h is up to 0.949, which are superior to all comparison algorithms.
- Multi-source data,
- Diffusion models,
- Stacked Long Short-Term Memory (LSTM),
- Attention mechanism,
- Feature extraction

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References(21)

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