SHORT-TERM POWER PREDICTION FOR DISTRIBUTED PV CLUSTERS BASED ON PCA-SHAPEDTW-QWGRU

Ouyang Jing; Qin Long; Wang Jianfeng; Yin Kang; Chu Lidong; Pan Guobing

doi:10.19912/j.0254-0096.tynxb.2023-0132

PDF(2623 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (5) : 458-467. DOI: 10.19912/j.0254-0096.tynxb.2023-0132

SHORT-TERM POWER PREDICTION FOR DISTRIBUTED PV CLUSTERS BASED ON PCA-SHAPEDTW-QWGRU

Ouyang Jing¹, Qin Long¹, Wang Jianfeng¹, Yin Kang², Chu Lidong¹, Pan Guobing¹

Author information +

History +

Abstract

A cluster power prediction model based on principal component analysis, shape dynamic time warping and quantum weighted gated recurrent unit （PCA-ShapeDTW-QWGRU） is established for distributed PV short-term power prediction. To address the issue of insufficiently fine cluster division and the difficulty of capturing the information contained in PV plant data, a cluster division method based on PCA with ordering points to identify the clustering structure （PCA-OPTICS） is proposed. Furthermore, to address the issue of low similarity between the selected representative power plants and the clusters, a selection method of representative power plants based on ShapeDTW is proposed, and ShapeDTW measures the similarity of the clusters. ShapeDTW quantifies the similarity distance, identifies the minimum value as the representative power station, and employs the QWGRU model optimised via the root-mean-square propagation （RMSprop-QWGRU） method, to in order to address the discrepancy in the transformation coefficient conversion of the representative power station with the cluster power, real-time transformation coefficients are employed for the prediction calculation of the cluster power value of the representative power station. The proposed method is employed to predict the cluster power value of the representative power station. The experimental results demonstrate that the proposed method can effectively enhance the precision of PV cluster power prediction.

Key words

PV power prediction / subgroup division / principal component analysis / dynamic time warping / quantum weighted gated recurrent unit

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Ouyang Jing, Qin Long, Wang Jianfeng, Yin Kang, Chu Lidong, Pan Guobing. SHORT-TERM POWER PREDICTION FOR DISTRIBUTED PV CLUSTERS BASED ON PCA-SHAPEDTW-QWGRU[J]. Acta Energiae Solaris Sinica. 2024, 45(5): 458-467 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0132

References

[1] IVANOVSKI K,HAILEMARIAM A,SMYTH R.The effect of renewable and non-renewable energy consumption on economic growth:non-parametric evidence[J]. Journal of cleaner production, 2021, 286: 124956.
[2] JANSSENS A, KAPSALAKI M.From energy crisis to sustainable indoor climate-40 years of AIVC Selected papers from the 40th AIVC Conference, 15-16 October 2019[J]. International journal of ventilation, 2021, 20(3-4): 159-160.
[3] AGGA A, ABBOU A, LABBADI M, et al.CNN-LSTM: an efficient hybrid deep learning architecture for predicting short-term photovoltaic power production[J]. Electric power systems research, 2022, 208: 107908.
[4] 韩梦瑶, 熊焦, 刘卫东. 中国光伏发电的时空分布、竞争格局及减排效益[J]. 自然资源学报, 2022, 37(5): 1338-1351.
HAN M Y, XIONG J, LIU W D.Spatio-temporal distribution, competitive development and emission reduction of China’s photovoltaic power generation[J]. Journal of natural resources, 2022, 37(5): 1338-1351.
[5] 张颖, 雷鸣宇, 杨子龙, 等. 改进连续集模型预测控制策略在平抑光伏功率波动中的应用[J]. 电网技术, 2019, 43(5): 1543-1549.
ZHANG Y, LEI M Y, YANG Z L, et al.An improved predictive control strategy of continuous control set model for PV power fluctuation damping[J]. Power system technology, 2019, 43(5): 1543-1549.
[6] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217.
LAI C W, LI J H, CHEN B, et al.Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1201-1217.
[7] 张小萌, 白恺, 柳玉, 等. 大规模分布式光伏短期集群功率预测综述[J]. 华北电力技术, 2017(6): 1-7.
ZHANG X M, BAI K, LIU Y, et al.Overview for large-scale distributed photovoltaic power prediction with clustered method[J]. North China electric power, 2017(6): 1-7.
[8] 彭小圣, 熊磊, 文劲宇, 等. 风电集群短期及超短期功率预测精度改进方法综述[J]. 中国电机工程学报, 2016, 36(23): 6315-6326, 6596.
PENG X S, XIONG L, WEN J Y, et al.A summary of the state of the art for short-term and ultra-short-term wind power prediction of regions[J]. Proceedings of the CSEE, 2016, 36(23): 6315-6326, 6596.
[9] LOBO M G, SANCHEZ I.Regional wind power forecasting based on smoothing techniques, with application to the Spanish peninsular system[J]. IEEE transactions on power systems, 2012, 27(4): 1990-1997.
[10] FU Q, LAI R P, SHAN Y H, et al.A spatial forecasting method for photovoltaic power generation combined of improved similar historical days and dynamic weights allocation[C]//2018 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), Singapore, 2018: 1195-1198.
[11] 卢俊杰, 蔡涛, 郎建勋, 等. 基于集群划分的光伏电站集群发电功率短期预测方法[J]. 高电压技术, 2022, 48(5): 1943-1951.
LU J J, CAI T, LANG J X, et al.Short-term power output forecasting of clustered photovoltaic solar plants based on cluster partition[J]. High voltage engineering, 2022, 48(5): 1943-1951.
[12] SHI J, LEE W J, LIU Y Q, et al.Forecasting power output of photovoltaic systems based on weather classification and support vector machines[J]. IEEE transactions on industry applications, 2012, 48(3): 1064-1069.
[13] 王献志, 曾四鸣, 周雪青, 等. 基于XGBoost联合模型的光伏发电功率预测[J]. 太阳能学报, 2022, 43(4): 236-242.
WANG X Z, ZENG S M, ZHOU X Q, et al.Power forecast of photovoltaic generation based on XGBoost combined model[J]. Acta energiae solaris sinica, 2022, 43(4): 236-242.
[14] 史凯钰, 张东霞, 韩肖清, 等. 基于LSTM与迁移学习的光伏发电功率预测数字孪生模型[J]. 电网技术, 2022, 46(4): 1363-1372.
SHI K Y, ZHANG D X, HAN X Q, et al.Digital twin model of photovoltaic power generation prediction based on LSTM and transfer learning[J]. Power system technology, 2022, 46(4): 1363-1372.
[15] 吕伟杰, 方一帆, 程泽. 基于模糊C均值聚类和样本加权卷积神经网络的日前光伏出力预测研究[J]. 电网技术, 2022, 46(1): 231-238.
LYU W J, FANG Y F, CHENG Z.Prediction of day-ahead photovoltaic output based on FCM-WS-CNN[J]. Power system technology, 2022, 46(1): 231-238.
[16] WANG Y S, LIAO W L, CHANG Y Q.Gated recurrent unit network-based short-term photovoltaic forecasting[J]. Energies, 2018, 11(8): 2163.
[17] 张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62-69.
ZHANG Y Q, CHENG Q Z, JIANG W J, et al.Photovoltaic power prediction model based on EMD-PCA-LSTM[J]. Acta energiae solaris sinica, 2021, 42(9): 62-69.
[18] 李海林, 梁叶, 王少春. 时间序列数据挖掘中的动态时间弯曲研究综述[J]. 控制与决策, 2018, 33(8): 1345-1353.
LI H L, LIANG Y, WANG S C.Review on dynamic time warping in time series data mining[J]. Control and decision, 2018, 33(8): 1345-1353.
[19] ZHAO J P, ITTI L.ShapeDTW: shape dynamic time warping[J]. Pattern recognition, 2018, 74:171-184.
[20] HUK M.Stochastic optimization of contextual neural networks with RMSprop[J]. Intelligent information and database systems, Cham: Springer, ACIIDS, 2020, 12034: 343-352.
[21] WANG S Y, ZHANG Z S.Short-term multiple load forecasting model of regional integrated energy system based on QWGRU-MTL[J]. Energies, 2021, 14(20): 6555.