基于气候相似性与SSA-CNN-LSTM的光伏功率组合预测

王晓霞; 俞敏; 冀明; 耿泉峰

doi:10.19912/j.0254-0096.tynxb.2022-0161

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (6) : 275-283. DOI: 10.19912/j.0254-0096.tynxb.2022-0161

基于气候相似性与SSA-CNN-LSTM的光伏功率组合预测

王晓霞¹, 俞敏¹, 冀明², 耿泉峰²

作者信息 +

PHOTOVOLTAIC POWER COMBINATION FORECASTING BASED ON CLIMATE SIMILARITY AND SSA-CNN-LSTM

Wang Xiaoxia¹, Yu Min¹, Ji Ming², Geng Quanfeng²

Author information +

文章历史 +

摘要

针对高分辨率气象数据匮乏影响光伏功率预测准确性的问题,提出一种融合气候相似性与奇异谱分析（SSA）、卷积神经网络（CNN）和长短期记忆网络（LSTM）的高分辨率光伏功率组合预测模型。运用SSA分解光伏序列为不同子序列,建立CNN-LSTM日前预测模型以捕捉光伏出力的连续性特征;利用气候相似性通过低分辨率气象数据选取相似日实现高分辨率光伏出力预测;通过灰色关联分析动态组合权重得到最终预测结果。仿真结果表明,该组合预测模型可有效提高日前高分辨率光伏功率预测的准确性,具有较高的预测精度。

Abstract

Aiming at the problem that the forecasting accuracy of photovoltaic power may be affected by the lack of high-resolution meteorological data, a high-resolution photovoltaic power combination forecasting model is proposed, which combines climate similarity with singular spectrum analysis （SSA）, convolutional neural networks（CNN） and long short-term memory（LSTM）. SSA is employed to decompose the photovoltaic sequence into different subsequences, and CNN-LSTM based on day ahead prediction model is established to capture the continuous characteristics of photovoltaic output. Moreover, the climate similarity is used to select similar days from low-resolution meteorological data to achieve high-resolution photovoltaic output prediction. Finally, the grey correlation analysis is utilized to obtain the combination weights to get the final prediction results. The simulation results show that the combined prediction model can effectively improve the prediction results of high-resolution photovoltaic power, and obtain high prediction accuracy.

导出引用

王晓霞, 俞敏, 冀明, 耿泉峰. 基于气候相似性与SSA-CNN-LSTM的光伏功率组合预测[J]. 太阳能学报. 2023, 44(6): 275-283 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0161

Wang Xiaoxia, Yu Min, Ji Ming, Geng Quanfeng. PHOTOVOLTAIC POWER COMBINATION FORECASTING BASED ON CLIMATE SIMILARITY AND SSA-CNN-LSTM[J]. Acta Energiae Solaris Sinica. 2023, 44(6): 275-283 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0161

中图分类号： TM615

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