| 引用本文: | 刘 伟,李洋洋.物理特征扩展的ASReLU-CNN-LSTM短期光伏功率预测研究[J].电力系统保护与控制,2026,54(02):58-69.[点击复制] |
| LIU Wei,LI Yangyang.Research on short-term photovoltaic power forecasting based on a physical feature expansion ASReLU-CNN-LSTM model[J].Power System Protection and Control,2026,54(02):58-69[点击复制] |
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| 摘要: |
| 为提高光伏发电系统在复杂多变气象条件下输出功率预测的精确性和稳定性，基于物理-数据融合的驱动策略，提出一种物理特征扩展的ASReLU-CNN-LSTM短期光伏功率预测方法。该方法首先通过改进太阳轨迹模型动态校正斜面辐照度，使其更准确地反映组件实际受光强度，接着结合光电转换模型与小型前馈网络扩展数据集的相对功率特征。其次，构建自适应平滑修正线性单元(adaptively smooth rectifier linear unit, ASReLU)，通过参数自适应平滑修正优化卷积神经网络(convolutional neural network, CNN)的负特征提取能力。最后，将物理特征扩展的数据集输入ASReLU-CNN-LSTM模型，实现光伏功率的预测。在两个不同气候区数据集上的实验结果表明，该预测方法具有较高的精确性和泛化能力。 |
| 关键词: 短期光伏功率预测 太阳轨迹模型 光电转换模型 自适应平滑修正线性单元 CNN-LSTM模型 |
| DOI：10.19783/j.cnki.pspc.250237 |
| 投稿时间：2025-03-06修订日期：2025-06-27 |
| 基金项目:国家自然科学基金项目资助(62473096) |
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| Research on short-term photovoltaic power forecasting based on a physical feature expansion ASReLU-CNN-LSTM model |
| LIU Wei1,2,LI Yangyang1 |
| (1. School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163000, China;
2. NEPU Sanya Offshore Oil & Gas Research Institute, Sanya 572000, China) |
| Abstract: |
| To enhance the accuracy and stability of photovoltaic (PV) power output forecasting under complex and highly variable meteorological conditions, a physics-data fusion-driven strategy is adopted, and a physical feature expansion ASReLU-CNN-LSTM method for short-term PV power forecasting is proposed. First, an improved solar trajectory model is used to dynamically correct the tilted surface irradiance so that it accurately reflects the actual irradiance received by PV modules. Subsequently, a PV conversion model and a lightweight feedforward network are employed to expand the dataset with relative power features. An adaptively smooth rectifier linear unit (ASReLU) is then designed, in which parameterized adaptive smoothing is introduced to enhance the negative-feature extraction capability of the convolutional neural network (CNN). Finally, the dataset augmented with physical features is fed into the ASReLU-CNN-LSTM model for PV power prediction. Experimental results on datasets from two distinct climatic regions demonstrate that the proposed method achieves high prediction accuracy and strong generalization capability. |
| Key words: short-term photovoltaic power forecasting solar trajectory model photovoltaic conversion model adaptively smooth rectified linear unit CNN-LSTM model |
