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Multi-step power forecasting for regional photovoltaic plants based on ITDE-GAT model

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  • Liu, Jincheng
  • Li, Teng

Abstract

With the integration of high proportion of distributed photovoltaic(PV), high-accuracy regional PV power forecasting technology can enhance the regional coordinated scheduling capability of the new power system. This paper proposes a regional PV power forecasting model based on an improved time-series dense encoder and graph attention network (ITDE-GAT), which takes into account the spatio-temporal correlations among the regional PV plants. Firstly, an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN) is used to extract the clear-sky and fluctuation components from PV data. Secondly, the combined ITDE-GAT is applied to perform the regional PV power forecasting. Considering the static information, an improved dense encoder network (ITDE) is constructed to extract the temporal and spatial relationships of regional PV. Graph attention network (GAT) is then utilized to explore the spatial correlations among the regional PV. Finally, case study from two actual PV region datasets shows that the proposed model achieves higher forecasting accuracy and exhibits stronger generalization capabilities. The results demonstrate that compared to various advanced deep learning methods, the R2 evaluation metric of the approach proposed in this paper demonstrates, respectively, maximum improvements of 3.4 %, 6.5 %, and 7.8 % for the 1 h, 3 h, and 6 h ahead predictions.

Suggested Citation

  • Liu, Jincheng & Li, Teng, 2024. "Multi-step power forecasting for regional photovoltaic plants based on ITDE-GAT model," Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s0360544224002391
    DOI: 10.1016/j.energy.2024.130468
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    References listed on IDEAS

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    7. Tian, Zhirui & Liang, Bingjie, 2025. "PVMTF: End-to-end long-sequence time-series forecasting frameworks based on patch technique and information fusion coding for mid-term photovoltaic power forecasting," Applied Energy, Elsevier, vol. 396(C).

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