NONPARAMETRIC PROBABILISTIC PREDICTION METHOD FOR NEW ENERGY BASED ON POWER STATION CLUSTERING AND TCN-QR-KDE IN PROVINCE-WIDE REGIONS

Su Huaying; Zhang Juntao; Li Dingyuan; Zhang Yan; Wang Rongrong; Cheng Chuntian

doi:10.19912/j.0254-0096.tynxb.2024-0839

PDF(2570 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 706-716. DOI: 10.19912/j.0254-0096.tynxb.2024-0839

NONPARAMETRIC PROBABILISTIC PREDICTION METHOD FOR NEW ENERGY BASED ON POWER STATION CLUSTERING AND TCN-QR-KDE IN PROVINCE-WIDE REGIONS

Su Huaying¹, Zhang Juntao², Li Dingyuan³, Zhang Yan¹, Wang Rongrong¹, Cheng Chuntian²

Author information +

History +

Abstract

The probability prediction problem of regional new energy power generation in the province faces difficulties such as multiple power station objects, high dimensional characteristic variables, and influence of regional meteorological factors. Therefore, this paper proposes a non-parametric probabilistic prediction method for regional new energy power in the province. Firstly, the historical meteorological factors of each station are used as input data, and the improved clustering method is used to construct wind power and photovoltaic clusters based on meteorologically similar areas, and then the meteorological information of each cluster is weighted and aggregated into available meteorological factors for the provincial regional prediction model. Based on this, the meteorological factors most related to new energy output are screened using Spearman and maximum information coefficient as the final input factors of the model to improve the model training efficiency. Finally, a nonparametric probabilistic prediction model based on time convolutional neural network （TCN）-quantile regression （QR）-kernel density estimation （KDE） is constructed. Taking all the wind power and photovoltaic plants in a southwestern province as application examples, the effectiveness of the proposed method is verified.

Key words

renewable energy / cluster analysis / neural networks / probabilistic prediction / meteorological similar zones

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Su Huaying, Zhang Juntao, Li Dingyuan, Zhang Yan, Wang Rongrong, Cheng Chuntian. NONPARAMETRIC PROBABILISTIC PREDICTION METHOD FOR NEW ENERGY BASED ON POWER STATION CLUSTERING AND TCN-QR-KDE IN PROVINCE-WIDE REGIONS[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 706-716 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0839

References

[1] 舒印彪, 陈国平, 贺静波, 等. 构建以新能源为主体的新型电力系统框架研究[J]. 中国工程科学, 2021, 23(6): 61-69.
SHU Y B, CHEN G P, HE J B, et al. Building a new electric power system based on new energy sources[J]. Strategic study of CAE, 2021, 23(6): 61-69.
[2] 舒印彪, 张丽英, 张运洲, 等. 我国电力碳达峰、碳中和路径研究[J]. 中国工程科学, 2021, 23(6): 1-14.
SHU Y B, ZHANG L Y, ZHANG Y Z, et al. Carbon peak and carbon neutrality path for China's power industry[J]. Strategic study of CAE, 2021, 23(6): 1-14.
[3] 郭威, 孙胜博, 陶鹏, 等. 基于多元变分模态分解和混合深度神经网络的短期光伏功率预测[J]. 太阳能学报, 2024, 45(4): 489-499.
GUO W, SUN S B, TAO P, et al. Short-term photovoltaic power forecasting based on multivariate variational mode decomposition and hybrid deep neural network[J]. Acta energiae solaris sinica, 2024, 45(4): 489-499.
[4] 庞传军, 张波, 余建明, 等. 基于结构化负荷模型的电力负荷概率区间预测[J]. 中国电力, 2021, 54(9): 89-95.
PANG C J, ZHANG B, YU J M, et al. Probabilistic interval forecasting of power load based on structured load model[J]. Electric power, 2021, 54(9): 89-95.
[5] 周育才, 肖添, 谢七月, 等. 基于聚类的HPO-BILSTM光伏功率短期预测[J]. 太阳能学报, 2024, 45(4): 512-518.
ZHOU Y C, XIAO T, XIE Q Y, et al. Clustering-based HPO-BILSTM short-term prediction of PV power[J]. Acta energiae solaris sinica, 2024, 45(4): 512-518.
[6] 万灿, 宋永华. 新能源电力系统概率预测理论与方法及其应用[J]. 电力系统自动化, 2021, 45(1): 2-16.
WAN C, SONG Y H. Theories, methodologies and applications of probabilistic forecasting for power systems with renewable energy sources[J]. Automation of electric power systems, 2021, 45(1): 2-16.
[7] 王钊, 王伟胜, 刘纯, 等. 风电功率的特征近邻搜索概率预测方法[J]. 电网技术, 2022, 46(3): 880-887.
WANG Z, WANG W S, LIU C, et al. Probabilistic forecast of wind power based on nearest neighbor feature searching[J]. Power system technology, 2022, 46(3): 880-887.
[8] 李丰君, 王磊, 赵健, 等. 基于天气融合和LSTM网络的分布式光伏短期功率预测方法[J]. 中国电力, 2022, 55(11): 149-154.
LI F J, WANG L, ZHAO J, et al. Research on distributed photovoltaic short-term power prediction method based on weather fusion and LSTM-Net[J]. Electric power, 2022, 55(11): 149-154.
[9] 万灿, 崔文康, 宋永华. 新能源电力系统概率预测: 基本概念与数学原理[J]. 中国电机工程学报, 2021, 41(19): 6493-6508.
WAN C, CUI W K, SONG Y H. Probabilistic forecasting for power systems with renewable energy sources: basic concepts and mathematical principles[J]. Proceedings of the CSEE, 2021, 41(19):6493-6508.
[10] WANG K, ZHANG Y, LIN F, et al.Nonparametric probabilistic forecasting for wind power generation using quadratic spline quantile function and autoregressive recurrent neural network[J]. IEEE transactions on sustainable energy, 2022, 13(4): 1930-1943.
[11] WU Y K, WU Y C, HONG J S, et al.Probabilistic forecast of wind power generation with data processing and numerical weather predictions[C]//2020 IEEE/IAS 56th Industrial and Commercial Power Systems Technical Conference (I&CPS). Las Vegas, NV, USA, 2020: 1-11.
[12] CUI W K, WAN C, SONG Y H.Ensemble deep learning-based non-crossing quantile regression for nonparametric probabilistic forecasting of wind power generation[J]. IEEE transactions on power systems, 2023, 38(4): 3163-3178.
[13] 王森, 孙永辉, 侯栋宸, 等. 基于多窗宽核密度估计的风电功率超短期自适应概率预测[J]. 高电压技术, 2024, 50(7): 3070-3079.
WANG S, SUN Y H, HOU D C, et al. Ultrashort-term adaptive probabilistic forecasting of wind power based on multi-band width kernel density estimation[J]. High voltage engineering, 2024, 50(7): 3070-3079.
[14] 杨锡运, 邢国通, 马雪, 等. 一种核极限学习机分位数回归模型及风电功率区间预测[J]. 太阳能学报, 2020, 41(11): 300-306.
YANG X Y, XING G T, MA X, et al. A model of quantile regression with kernel extreme learning machine and wind power interval prediction[J]. Acta energiae solaris sinica, 2020, 41(11): 300-306.
[15] KHORRAMDEL B, ZARE A, CHUNG C Y, et al.A generic convex model for a chance-constrained look-ahead economic dispatch problem incorporating an efficient wind power distribution modeling[J]. IEEE transactions on power systems, 2020, 35(2): 873-886.
[16] FAUSTINE A, PEREIRA L.FPSeq2Q: fully parameterized sequence to quantile regression for net-load forecasting with uncertainty estimates[J]. IEEE transactions on smart grid, 2022, 13(3): 2440-2451.
[17] VON KRANNICHFELDT L, WANG Y, ZUFFEREY T, et al.Online ensemble approach for probabilistic wind power forecasting[J]. IEEE transactions on sustainable energy, 2022, 13(2): 1221-1233.
[18] YU Y X, YANG M, HAN X S, et al.A regional wind power probabilistic forecast method based on deep quantile regression[J]. IEEE transactions on industry applications, 2021, 57(5): 4420-4427.
[19] 崔杨, 陈正洪, 许沛华. 基于机器学习的集群式风光一体短期功率预测技术[J]. 中国电力, 2020, 53(3): 1-7.
CUI Y, CHEN Z H, XU P H. Short-term power prediction for wind farm and solar plant clusters based on machine learning method[J]. Electric power, 2020, 53(3): 1-7.
[20] 王钊, 王勃, 冯双磊, 等. 区域多风电场功率的分位数回归概率预测方法[J]. 电网技术, 2020, 44(4): 1368-1375.
WANG Z, WANG B, FENG S L, et al. Probabilistic forecasts based on quantile regression for regional wind farms[J]. Power system technology, 2020, 44(4): 1368-1375.
[21] 李青, 张新燕, 摆志俊, 等. 基于MQ-WaveNet的风电集群发电功率多步概率预测[J]. 电力系统自动化, 2023, 47(8): 156-168.
LI Q, ZHANG X Y, BAI Z J, et al. Multi-step probability prediction of power generation for wind power clusters based on multi-horizon quantile-WaveNet[J]. Automation of electric power systems, 2023, 47(8): 156-168.
[22] YU Y X, HAN X S, YANG M, et al.Probabilistic prediction of regional wind power based on spatiotemporal quantile regression[J]. IEEE transactions on industry applications, 2020, 56(6): 6117-6127.
[23] 王涛, 李薇, 许野, 等. 计及相似日的VMD-FE-LSTM光伏出力组合预测模型研究[J]. 太阳能学报, 2024, 45(5): 490-499.
WANG T, LI W, XU Y, et al. Study on photovoltaic power prediction of VMD-FE-LSTM considering similar days[J]. Acta energiae solaris sinica, 2024, 45(5): 490-499.
[24] 任密蜂, 王家辉, 叶泽甫, 等. 一种适用于单/多光伏电站的迁移超短期光伏预测建模框架[J]. 太阳能学报, 2024, 45(6): 359-367.
REN M F, WANG J H, YE Z F, et al. Transfer ultra-short term photovoltaic prediction modeling framework for single/multiple photovoltaic power stations[J]. Acta energiae solaris sinica, 2024, 45(6): 359-367.