LI Zengwei;WANG Yayun;ZHANG Rongfu;MA Yuanming;FANG Chen;WEI Yongyu;

• 1:Information & Telecommunication Branch
• 2: State Grid Qinghai Electric Power Company

Abstract：

The random and volatile distributed photovoltaic(PV) power pose challenges for accurate forecasting and dispatch decision-making in power system operations. To address this, A multi-timescale photovoltaic power prediction method based on SE-CNN-BiLSTM and improved Transformer is proposed. Firstly, leveraging the diurnal trend similarity characteristics of PV power, a feature extraction method incorporating a channel attention mechanism is proposed to construct a prediction model for PV power trend features. Subsequently, based on the short-term fluctuation characteristics of PV power, a fluctuation feature extraction method based on similar time-period matching(STM) is proposed, utilizing the weather-induced fluctuation features of PV power to build a prediction model based on an improved Transformer. Then, by fusing the long-and short-timescale trend features and fluctuation features of PV power, a multi-timescale fusion method for PV power prediction is constructed. Finally, the proposed model is validated using actual operational data from a PV power station and simulation data. Results demonstrate that the proposed method effectively enhances the representational capacity and prediction accuracy of the forecasting model.

Key Words： PV power prediction;attention mechanism;LSTM;improved Transformer;feature fusion

Abstract:

Keywords:

Foundation: 国网青海省电力公司科技项目（52281424000C）

Authors: LI Zengwei;WANG Yayun;ZHANG Rongfu;MA Yuanming;FANG Chen;WEI Yongyu;

DOI: 10.19585/j.zjdl.202603011

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