融合DT-BO-GRU的中长期光伏功率滚动预测模型

李超; 涂腾; 彭勋辉; 李振; 晁梓博; 刘淑玉

doi:10.19912/j.0254-0096.tynxb.2024-0068

PDF(1773 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (5) : 275-284. DOI: 10.19912/j.0254-0096.tynxb.2024-0068

融合DT-BO-GRU的中长期光伏功率滚动预测模型

李超, 涂腾, 彭勋辉, 李振, 晁梓博, 刘淑玉

作者信息 +

MEDIUM-AND LONG-TERM ROLLING PREDICTION MODEL OF PV POWER FUSED WITH DT-BO-GU

Li Chao, Tu Teng, Peng Xunhui, Li Zhen, Chao Zibo, Liu Shuyu

Author information +

文章历史 +

摘要

提出一种基于决策树提取的贝叶斯优化GRU中长期光伏发电功率滚动预测模型。首先借助决策树模型对光伏组件模型进行参数提取,重新组成特征数据集;其次引入贝叶斯优化算法构建新的GRU神经网络模型;最后对树模型提取的光伏参数进行光伏功率预测。实验结果表明,所提出的混合模型在极端地区等特殊场景下具有高精度的预测效果,且实验仿真结果拟合曲线更接近真实值,模型整体评价指标误差较低。因此,该文提出的融合DT-BO-GRU模型具有更高预测精度,为在北方地区对光伏发电功率预测提供了可能。

Abstract

In view of the low prediction accuracy and slow convergence speed of traditional photovoltaic power prediction models, the deep neural network model has problems such as gradient explosion or vanishing gradient. In this paper, a Bayesian-optimized GRU medium- and long-term photovoltaic power rolling prediction model based on decision tree extraction is proposed. Firstly, the parameters of the photovoltaic module model are extracted with the help of the decision tree model to reconstitute the feature data set, subsequently the Bayesian optimization algorithm is introduced to construct a new GRU neural network model, Finally, the photovoltaic power prediction is carried out on the photovoltaic parameters extracted from the tree model. Experimental results show that the hybrid model proposed in this paper has a high-precision prediction effect in special scenarios such as extreme areas, The fitting curve of the experimental simulation results is closer to the real value, and the error of the overall evaluation index of the model is low. Therefore, the fused DT-BO-GRU model proposed in this paper has higher prediction accuracy and provides the feasible method for predicting photovoltaic power generation in northern China.

导出引用

李超, 涂腾, 彭勋辉, 李振, 晁梓博, 刘淑玉. 融合DT-BO-GRU的中长期光伏功率滚动预测模型[J]. 太阳能学报. 2025, 46(5): 275-284 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0068

Li Chao, Tu Teng, Peng Xunhui, Li Zhen, Chao Zibo, Liu Shuyu. MEDIUM-AND LONG-TERM ROLLING PREDICTION MODEL OF PV POWER FUSED WITH DT-BO-GU[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 275-284 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0068

中图分类号： TM615

参考文献

[1] DHAKED D K, DADHICH S, BIRLA D.Power output forecasting of solar photovoltaic plant using LSTM[J]. Green energy and intelligent transportation, 2023, 2(5): 100113.
[2] NI Q, ZHUANG S X, SHENG H M, et al.An optimized prediction intervals approach for short term PV power forecasting[J]. Energies, 2017, 10(10): 1669.
[3] ALARAJ M, ALSAIDAN I, KUMAR A, et al.Advanced intelligent approach for solar PV power forecasting using meteorological parameters for qassim region, Saudi Arabia[J]. Sustainability, 2023, 15(12): 9234.
[4] TAVARES I, MANFREDINI R, ALMEIDA J, et al.Comparison of PV power generation forecasting in a residential building using ANN and DNN[J]. IFAC-PapersOnLine, 2022, 55(9): 291-296.
[5] 张成, 白建波, 兰康, 等. 基于数据挖掘和遗传小波神经网络的光伏电站发电量预测[J]. 太阳能学报, 2021, 42(3): 375-382.
ZHANG C, BAI J B, LAN K, et al.Photovoltaic power generation prediction based on data mining and genetic wavelet neural network[J]. Acta energiae solaris sinica, 2021, 42(3): 375-382.
[6] 王俊杰, 毕利, 张凯, 等. 基于多特征融合和XGBoost-LightGBM-ConvLSTM的短期光伏发电量预测[J]. 太阳能学报, 2023, 44(7): 168-174.
WANG J J, BI L, ZHANG K, et al.Short-term photovoltaic power generation prediction based on multi-feature fusion and XGBoost-LightGBM-ConvLSTM[J]. Acta energiae solaris sinica, 2023, 44(7): 168-174.
[7] 刘国海, 孙文卿, 吴振飞, 等. 基于Attention-GRU的短期光伏发电功率预测[J]. 太阳能学报, 2022, 43(2): 226-232.
LIU G H, SUN W Q, WU Z F, et al.Short-term photovoltaic power forecasting based on Attention-GRU model[J]. Acta energiae solaris sinica, 2022, 43(2): 226-232.
[8] 姚宏民, 杜欣慧, 秦文萍. 基于密度峰值聚类及GRNN神经网络的光伏发电功率预测方法[J]. 太阳能学报, 2020, 41(9): 184-190.
YAO H M, DU X H, QIN W P.PV power forecasting approach based on density peaks clustering and general regression neural network[J]. Acta energiae solaris sinica, 2020, 41(9): 184-190.
[9] 吴珺玥, 赵二刚, 郭增良, 等. 基于Spearman系数和TCN的光伏出力超短期多步预测[J]. 太阳能学报, 2023, 44(9): 180-186.
WU J Y, ZHAO E G, GUO Z L, et al.Ultra-short-term photovoltaic power multi-step prediction based on spearman coefficient and TCN[J]. Acta energiae solaris sinica, 2023, 44(9): 180-186.
[10] 刘刚, 闵金, 宋伟, 等. 基于K-Means和GBRT的分布式光伏中短期发电量预测[J]. 能源与环保, 2023, 45(3): 210-215, 221.
LIU G, MIN J, SONG W, et al.Medium-short term power generation prediction of distributed photovoltaic based on K-Means and GBRT[J]. China energy and environmental protection, 2023, 45(3): 210-215, 221.
[11] 万子健, 李连营, 杨敏, 等. 车辆轨迹数据提取道路交叉口特征的决策树模型[J]. 测绘学报, 2019, 48(11): 1391-1403.
WAN Z J, LI L Y, YANG M, et al.Decision tree model for extracting road intersection feature from vehicle trajectory data[J]. Acta geodaetica et cartographica sinica, 2019, 48(11): 1391-1403.
[12] 王颖, 陆超, 张欣然, 等. 基于特征提取的负荷模型参数相关性规则挖掘[J]. 供用电, 2020, 37(4): 3-9.
WANG Y, LU C, ZHANG X R, et al.Feature extracting based correlation rules mining for load model parameters[J]. Distribution & utilization, 2020, 37(4): 3-9.
[13] 陆秋贤, 马刚, 涂孟夫. 基于IEWT-FE-BO-LSTM模型的超短期风功率预测[J]. 水电能源科学, 2023, 41(1): 217-220.
LU Q X, MA G, TU M F.Ultra-short-term wind power prediction based on IEWT-FE-BO-LSTM model[J]. Water resources and power, 2023, 41(1): 217-220.
[14] 吴永洪, 张智斌. 基于贝叶斯优化的CNN-GRU短期电力负荷预测[J]. 现代电子技术, 2023, 46(20): 125-129.
WU Y H, ZHANG Z B.CNN-GRU short-term power load forecasting based on Bayesian optimization[J]. Modern electronics technique, 2023, 46(20): 125-129.
[15] 李宏扬, 高丙朋. 基于改进VMD和SNS-Attention-GRU的短期光伏发电功率预测[J]. 太阳能学报, 2023, 44(8): 292-300.
LI H Y, GAO B P.Short-term pv power forecasting based on improved VMD and SNS-Attention-GRU[J]. Acta energiae solaris sinica, 2023, 44(8): 292-300.
[16] 董雪, 赵生校, 陆艳艳, 等. 基于奇异谱分解和双重注意力机制的超短期光伏功率预测方法[J]. 激光与光电子学进展, 2023, 60(5): 310-317.
DONG X, ZHAO S X, LU Y Y, et al.Ultra-short-term forecasting method for photovoltaic power based on singular spectrum decomposition and double attention mechanism[J]. Laser & optoelectronics progress, 2023, 60(5): 310-317.