EN基于GSS的RIME-CNN-LSTM-Attention光伏出力组合预测模型
基于GSS的RIME-CNN-LSTM-Attention光伏出力组合预测模型
- 太阳能学报 2025年第10期 页码：250-258
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DOI：doi:10.19912/j.0254-0096.tynxb.2024-1036
中图分类号： M615-
网络出版：2025-10-31，
纸质出版：2025-10-31
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黄毅, 马一力, 苗安康, 等. 基于GSS的RIME-CNN-LSTM-Attention光伏出力组合预测模型[J]. 太阳能学报, 2025,(10):250-258.
黄毅, 马一力, 苗安康, et al. 基于GSS的RIME-CNN-LSTM-Attention光伏出力组合预测模型[J]. 2025, (10): 250-258.
黄毅, 马一力, 苗安康, 等. 基于GSS的RIME-CNN-LSTM-Attention光伏出力组合预测模型[J]. 太阳能学报, 2025,(10):250-258. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1036.
黄毅, 马一力, 苗安康, et al. 基于GSS的RIME-CNN-LSTM-Attention光伏出力组合预测模型[J]. 2025, (10): 250-258. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1036.
摘要
针对传统气象学天气分型的局限性
同时考虑到优化算法的场景匹配度
提出基于广义场景划分（GSS）的RIME-CNN-LSTM-Attention光伏出力组合预测模型。首先
以科学性、典型性为导向
提出基于广义场景划分指标的场景划分方法
为解决传统分类聚合方法存在的日气象相似性和耦合性问题提供解决思路
有效支撑高精度预测模型的构建;其次
结合长短期记忆网络（LSTM）时序特征提取能力以及卷积神经网络（CNN）的空间特征提取优势
引入注意力机制
构建光伏出力基础预测模型
在增强模型的关键信息捕捉能力的同时有效提升预测的整体精度;此外
考虑到强不确定性条件下光伏出力的非线性特点
引入一种基于霜冰物理现象的优化算法
有效提升优化算法对复杂场景的适应能力。仿真结果显示
基于GSS的RIME-CNN-LSTM-Attention组合预测模型能有效提高光伏出力预测的准确性
在光伏出力波动性较强的场景下展现出较高的预测精度和实用性。
Abstract
In response to the limitations of traditional meteorological weather typing and considering the scenario-matching degree of optimization algorithms
RIME algorithm convolutional neural network long short term memory attention（RIME-CNN-LSTM-Attention）photovoltaic output combination prediction model based on generalized scene segmentation（GSS） is proposed. Firstly
guided by scientific and typical principles
a generalized scenario segmentation method based on a generalized scenario segmentation indicator is introduced
providing solution ideas for solving the daily weather similarity and coupling problems existing in traditional classification and aggregation methods
effectively supporting the construction of high-precision prediction models. Secondly
a foundational photovoltaic power output prediction model is constructed by combining the temporal feature extraction capabilities of long short-term memory （LSTM） networks with the spatial feature extraction advantages of convolutional neural networks （CNN） and introducing an attention mechanism. This approach enhances the model’s ability to capture critical information and effectively improves the overall accuracy of the predictions. Additionally
considering the nonlinear characteristics of photovoltaic output under conditions of strong uncertainty
an optimization algorithm based on the physical phenomenon of frost ice is introduced
significantly enhancing the optimization algorithm’s adaptability to complex scenarios. Simulation results show that the GSS-based RIME-CNN-LSTM-Attention combination prediction model can effectively improve the accuracy of photovoltaic output predictions
demonstrating high predictive accuracy and practicality in scenarios where photovoltaic output is highly volatile.
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references
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