Interpretable ultra-short-term photovoltaic power forecas…

Accurate photovoltaic (PV) power forecasting is essential for grid operation but remains difficult due to nonlinear multi-scale dynamics and seasonal distribution shifts. This work presents MKAN-iTransformer, a cascaded framework that integrates two existing …

Accurate photovoltaic (PV) power forecasting is essential for grid operation but remains difficult due to nonlinear multi-scale dynamics and seasonal distribution shifts. This work presents MKAN-iTransformer, a cascaded framework that integrates two existing components—the Multi-Scale Kolmogorov–Arnold Network (MKAN) for scale-aware temporal representation learning and iTransformer for variable-wise attention and inter-variable dependency modeling—under a 15-minute single-step setting.

Experiments on a real-world 30 MW PV plant dataset from the Chinese State Grid Renewable Energy Generation Forecasting Competition use chronological splits within each season. MKAN-iTransformer achieves the best overall performance in spring, autumn, and winter. In spring, it reaches MSE=2.892, RMSE=1.701, MAE=0.864, and ({R^{2}}=0.947), improving over LSTM by 23.5%/12.5%/20.5% (MSE/RMSE/MAE). In autumn, it attains MSE=2.884, RMSE=1.698, MAE=0.774, and ({R^{2}}=0.962), reducing errors vs.

iTransformer by 16.5%/8.7%/12.4%. In winter, it achieves MSE=1.721, RMSE=1.312, MAE=0.443, and ({R^{2}}=0.969), yielding 81.6%/57.1%/71.9% error reductions vs. Transformer. Ablation further confirms the complementarity between MKAN and iTransformer and shows that direct KAN integration can be unstable under winter shifts (KAN-iTransformer: MSE=7.082, ({R^{2}}=0.872)). The dataset used in this study is sourced from the State Grid Corporation of China New Energy Power Generation Forecasting Competition (^{?}).

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Comput. Methods Appl. Mech. Eng. 432, 117397. https://doi.org/10.1016/j.cma.2024.117397 (2024). This research was funded by the National Natural Science Foundation of China, grant number 51967004. This research was funded by the National Natural Science Foundation of China, grant number 51967004. L.L. (first author) conceived the research idea, developed the MKAN-iTransformer model, implemented all experiments, and wrote the initial draft of the manuscript.

M.L. (corresponding author) supervised the entire research process, provided key guidance on model design and result analysis, and substantially revised the manuscript. Z.H. and H.Z. contributed to data preprocessing, experimental support, and manuscript review. All authors have read and approved the final version of the manuscript. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Liu, L., Liu, M., Han, Z. et al. Interpretable ultra-short-term photovoltaic power forecasting with multi-scale temporal modeling and variable-wise attention. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39797-6

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This report covers the latest developments in pakistan. The information presented highlights key changes and updates that are relevant to those following this topic.

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