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Keywords

photovoltaic forecasting; multivariate time series; cross-dimensional feature; cross-scale feature; two-stage attention me chanism

Abstract

To address the limitations of current photo voltaic power forecasting methods, which rely heavily on meteorological monitoring and classification techniques and are unable to achieve accurate all-weather forecasting under large-scale complex data, a Multiformer-TSA method based on a two-stage attention (TSA) mechanism is proposed for photovoltaic power forecasting. First, a cross-scale embedding layer is constructed to generate staged sampling tokens and collect photovoltaic sequences at different scales, thus extracting cross-scale features. Then, point-based segments of different dimensions in multivariate time series are embedded to form new feature vectors, capturing cross-dimensional dependencies. Finally, cross-scale and cross-dimensional dependency information is fused through the TSA mechanism to achieve accurate all-weather photovoltaic power forecasting. Multi-scale forecasting comparison experiments and ablation experiments are conducted on a publicly available photovoltaic power dataset from Alice Springs, Australia. The experimental results demonstrate that the proposed method accurately captures cross-scale and cross-dimensional features of multivariate time series and improves the multi-scale forecasting accuracy of photovoltaic power generation. Compared with existing methods, the proposed method achieves the best performance in terms of root mean square error (RMSE) and mean absolute error (MAE).

DOI

10.19781/j.issn.1673-9140.2026.01.013

First Page

130

Last Page

139

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