基于GWO-GRU的光伏发电功率预测

陈庆明; 廖鸿飞; 孙颖楷; 曾亚森

doi:10.19912/j.0254-0096.tynxb.2023-1248

PDF(2057 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (7) : 438-444. DOI: 10.19912/j.0254-0096.tynxb.2023-1248

基于GWO-GRU的光伏发电功率预测

陈庆明¹, 廖鸿飞¹, 孙颖楷², 曾亚森¹

作者信息 +

PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON GWO-GRU

Chen Qingming¹, Liao Hongfei¹, Sun Yingkai², Zen Yasen¹

Author information +

文章历史 +

摘要

针对长短期记忆网络（LSTM）应用于光伏发电功率预测时存在的耗时长或精准度低的问题,提出基于灰狼算法（GWO）优化门控循环单元（GRU）的光伏发电功率预测模型。通过GWO算法优化GRU模型的超参数,以近似最优参数建立光伏发电功率预测模型。结果表明,长时功率预测时,GWO-GRU模型的均方根误差更低、拟合系数更高、耗时更少,比传统LSTM模型的平均绝对误差降低10.20%;短时功率预测时,GWO-GRU模型在3种典型天气条件下不仅预测的平均误差最低、稳定性最强,而且比GWO-LSTM模型的平均用时节省17.24%。不同时长的功率预测表明,GWO-GRU相对于LSTM光伏功率预测效果更佳。

Abstract

The long short-term memory network （LSTM） model has the problem of long time consumption or low accuracy when applied to the prediction of photovoltaic power generation. A photovoltaic power power prediction model based on the grey wolf algorithm （GWO） optimized gated recurrent unit （GRU） was proposed. The photovoltaic power prediction model is established by the approximate optimal hyperparameter, which is obtained by the GWO algorithm. The results show that in terms of long-term power prediction, the GWO-GRU model has lower root mean square error, higher fitting coefficients, and less time consumption, with an average absolute error reduction of 10.20% compared to traditional LSTM models. In terms of short-term power prediction, the GWO-GRU model not only has the lowest average prediction error and the strongest stability under three typical weather conditions, but also saves 17.24% of the average time compared to the GWO-LSTM model. Power predictions of different durations indicate that GWO-GRU performs better in predicting photovoltaic power compared to LSTM.

导出引用

陈庆明, 廖鸿飞, 孙颖楷, 曾亚森. 基于GWO-GRU的光伏发电功率预测[J]. 太阳能学报. 2024, 45(7): 438-444 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1248

Chen Qingming, Liao Hongfei, Sun Yingkai, Zen Yasen. PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON GWO-GRU[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 438-444 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1248

中图分类号： TM615 TP391.4

参考文献

[1] 周浩杰, 杨建卫, 王尊, 等. 基于LSTM光伏发电功率超短期预测模型研究[J]. 电源技术, 2023, 47(6): 785-789.
ZHOU H J, YANG J W, WANG Z, et al.Research on ultra-short term prediction model of photovoltaic power generation based on LSTM[J]. Chinese journal of power sources, 2023, 47(6): 785-789.
[2] 宋绍剑, 李博涵. 基于LSTM网络的光伏发电功率短期预测方法的研究[J]. 可再生能源, 2021, 39(5): 594-602.
SONG S J, LI B H.Short-term forecasting method of photovoltaic power based on LSTM[J]. Renewable energy resources, 2021, 39(5): 594-602.
[3] 王福忠, 王帅峰, 张丽. 基于VMD-LSTM与误差补偿的光伏发电超短期功率预测[J]. 太阳能学报, 2022, 43(8): 96-103.
WANG F Z, WANG S F, ZHANG L.Ultra short term power prediction of photovoltaic power generation based on VMD-LSTM and error compensation[J]. Acta energiae solaris sinica, 2022, 43(8): 96-103.
[4] 赵晋斌, 张建平, 毛玲, 等. 基于PSO-Soft attention双向LSTM算法的光伏发电量预测研究[J]. 智慧电力, 2022, 50(3): 1-7.
ZHAO J B, ZHANG J P, MAO L, et al.Photovoltaic power generation forecasting based on PSO-Soft attention bidirectional LSTM algorithm[J]. Smart power, 2022, 50(3): 1-7.
[5] 刘玢岑, 季陈林, 彭钰祥, 等. 基于ACO-KF-GRU-EC的光伏发电量组合预测模型[J]. 计算机仿真, 2022, 39(10): 118-123, 147.
LIU B C, JI C L, PENG Y X, et al.Combined perdicting model of photovoltaic power generation based on ACO-KF-GRU-EC[J]. Computer simulation, 2022, 39(10): 118-123, 147.
[6] 张进, 刘运, 彭曙蓉. 基于特征挖掘的GRU-A光伏发电功率预测[J]. 实验室研究与探索, 2020, 39(5): 25-30, 49.
ZHANG J, LIU Y, PENG S R.Photovoltaic power prediction based on feature mining and GRU-A[J]. Research and exploration in laboratory, 2020, 39(5): 25-30, 49.
[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] 文爽, 马逸骋, 孙志强. 基于GWO-EEMD-BP神经网络的光伏发电功率短期预测[J]. 中南大学学报(自然科学版), 2022, 53(12): 4799-4808.
WEN S, MA Y C, SUN Z Q.Short-term prediction of photovoltaic power based on GWO-EEMD-BP[J]. Journal of Central South University (science and technology), 2022, 53(12): 4799-4808.
[9] 李容爽, 谢源, 金鹏飞, 等. GWO-ELMAN神经网络在光伏最大功率点跟踪中的应用[J]. 上海电机学院学报, 2019, 22(5): 249-254.
LI R S, XIE Y, JIN P F, et al.Application of GWO-ELMAN neural network in photovoltaic maximum power point tracking[J]. Journal of Shanghai Dianji University, 2019, 22(5): 249-254.
[10] 王粟, 隗磊锋, 曾亮. 基于GWO-SVM与随机森林的组合光伏功率预测模型[J]. 昆明理工大学学报(自然科学版), 2021, 46(5): 82-88.
WANG S, WEI L F, ZENG L.A combined model for photovoltaic power forecasting based on GWO-SVM and random forest[J]. Journal of Kunming University of Science and Technology (natural sciences), 2021, 46(5): 82-88.
[11] 薛阳, 燕宇铖, 贾巍, 等. 基于改进灰狼算法优化长短期记忆网络的光伏功率预测[J]. 太阳能学报, 2023, 44(7): 207-213.
XUE Y, YAN Y C, JIA W, et al.Photovoltaic power prediction model based on igwo-lstm[J]. Acta energiae solaris sinica, 2023, 44(7): 207-213.
[12] MIRJALILI S, MIRJALILI S M, LEWIS A.Grey wolf optimizer[J]. Advances in engineering software, 2014, 69: 46-61.
[13] 徐达宇, 丁帅. 改进GWO优化SVM的云计算资源负载短期预测研究[J]. 计算机工程与应用, 2017, 53(7): 68-73.
XU D Y, DING S.Research on improved GWO-optimized SVM-based short-term load prediction for cloud computing[J]. Computer engineering and applications, 2017, 53(7): 68-73.
[14] CHO K, VAN MERRIENBOER B, BAHDANAU D, et al.On the properties of neural machine translation: encoder-decoder approaches[C]//Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation. Doha, Qatar,2014: 103-111.
[15] 赵兵, 王增平, 纪维佳, 等. 基于注意力机制的CNN-GRU短期电力负荷预测方法[J]. 电网技术, 2019, 43(12): 4370-4376.
ZHAO B, WANG Z P, JI W J, et al.A short-term power load forecasting method based on attention mechanism of CNN-GRU[J]. Power system technology, 2019, 43(12): 4370-4376.
[16] 周满国, 黄艳国, 段锦锋. 基于GRU-RF模型的太阳辐照度短时预测[J]. 太阳能学报, 2022, 43(7): 166-173.
ZHOU M G, HUANG Y G, DUAN J F.Short term prediction of soral irradiance based on GRU-RF model[J]. Acta energiae solaris sinica, 2022, 43(7): 166-173.
[17] 王依宁, 解大, 王西田, 等. 基于PCA-LSTM模型的风电机网相互作用预测[J]. 中国电机工程学报, 2019, 39(14): 4070-4081.
WANG Y N, XIE D, WANG X T, et al.Prediction of interaction between grid and wind farms based on PCA-LSTM model[J]. Proceedings of the CSEE, 2019, 39(14): 4070-4081.