基于最大信息系数的短期太阳辐射协同估计

李津, 史加荣, 张琰妮, 云斯宁

太阳能学报 ›› 2023, Vol. 44 ›› Issue (9) : 286-294.

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (9) : 286-294. DOI: 10.19912/j.0254-0096.tynxb.2022-0693

基于最大信息系数的短期太阳辐射协同估计

  • 李津1, 史加荣1, 张琰妮1, 云斯宁2
作者信息 +

SHORT-TERM SOLAR RADIATION SYNERGY ESTIMATION BASED ONMAXIMUM INFORMATION COEFFICIENT

  • Li Jin1, Shi Jiarong1, Zhang Yanni1, Yun Sining2
Author information +
文章历史 +

摘要

提出一种短期太阳辐射估计的协同方法,即利用邻近站点数据来估计目标站点的太阳辐射。先利用最大信息系数对所有站点的相关数据进行特征选择。然后将特征选择后的数据作为输入,采用不同的机器学习模型进行估计。最后在实际数据上将协同估计的误差与仅采用目标站点的估计误差进行比较。实验结果表明协同估计对所有目标站点都有更高的精度和更低的误差。

Abstract

To make precise and reliable estimation of solar radiation, this paper proposes a short-term solar radiation synergy estimation method, which implements adjacent station data to estimate solar radiation at target station. First, the maximum information coefficient is used to perform feature selection on relevant data from all stations. Then the data after feature selection are utilized as input for estimation using different machine learning models. The errors of the synergistic estimation are finally compared to the error taking only the target station data on real data. Experimental results indicate that the performance of the synergy estimation has a more precision and low error for all target stations, compared without synergy estimation.

关键词

太阳辐射 / 机器学习 / 特征选择 / 协同估计 / 最大信息系数

Key words

solar radiation / machine learning / feature selection / synergy estimation / maximum information coefficient

引用本文

导出引用
李津, 史加荣, 张琰妮, 云斯宁. 基于最大信息系数的短期太阳辐射协同估计[J]. 太阳能学报. 2023, 44(9): 286-294 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0693
Li Jin, Shi Jiarong, Zhang Yanni, Yun Sining. SHORT-TERM SOLAR RADIATION SYNERGY ESTIMATION BASED ONMAXIMUM INFORMATION COEFFICIENT[J]. Acta Energiae Solaris Sinica. 2023, 44(9): 286-294 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0693
中图分类号: TM615   

参考文献

[1] 单存博. 基于数据挖掘的太阳辐射预测方法研究[D]. 北京: 华北电力大学, 2021.
SHAN C B.Research on solar radiation forecasting method based on data mining[D]. Beijing: North China Electric Power University, 2021.
[2] VOYANT C, NOTTON G, KALOGIROU S, et al.Machine learning methods for solar radiation forecasting: a review[J]. Renewable energy, 2017, 105: 569-582.
[3] 于瑛, 杨柳, 霍旭杰, 等.日总辐射推算模型在中国的适用性研究[J]. 太阳能学报, 2018, 39(9): 2523-2529.
YU Y, YANG L, HUO X J, et al.Analysis of daily global radiation estimation models applicability in China[J]. Acta energiae solaris sinica, 2018, 39(9): 2523-2529.
[4] 曹其梦, 于瑛, 杨柳. 太阳逐时总辐射计算模型适用性分析: 以我国部分地区为例[J]. 太阳能学报, 2018, 39(4): 917-924.
CAO Q M,YU Y,YANG L.Applicability analysis of hourly total solar radiation calculation model: taking some regions of China as example[J]. Acta energiae solaris sinica, 2018, 39(4): 917-924.
[5] 胡尧, 李红莲, 王赏玉, 等. 辐射数据缺失时TMY与逐时值生成方法分析[J]. 哈尔滨工业大学学报, 2022, 54(6): 163-170.
HU Y, LI H L, WANG S Y, et al.Analysis of typical meteorological year and hourly value generation method with radiation data missing[J]. Journal of Harbin Institute of Technology, 2022, 54(6): 163-170.
[6] MUKHTAR M, OLUWASANMI A, YIMEN N, et al.Development and comparison of two novel hybrid neural network models for hourly solar radiation prediction[J]. Applied sciences, 2022, 12(3): 1435.
[7] WATANABE T, TAKENAKA H, NOHARA D.Post-processing correction method for surface solar irradiance forecast data from the numerical weather model using geostationary satellite observation data[J]. Solar energy, 2021, 223: 202-216.
[8] 蒋俊霞, 高晓清, 吕清泉, 等. 基于地基云图的云跟踪与太阳辐照度超短期预报方法研究[J]. 太阳能学报, 2020, 41(5): 351-358.
JIANG J X, GAO X Q, LYU Q Q, et al.Study on cloud tracking and solar irradiance ultra-short-term forecasting based on TSI images[J]. Acta energiae solaris sinica, 2020, 41(5): 351-358.
[9] BELMAHDI B, LOUZAZNI M, EL BOUARDI A.One month-ahead forecasting of mean daily global solar radiation using time series models[J]. Optik, 2020, 219: 165207.
[10] 倪超, 王聪, 朱婷婷, 等. 基于CNN-Bi-LSTM的太阳辐照度超短期预测[J]. 太阳能学报, 2022, 43(3): 197-202.
NI C, WANG C, ZHU T T, et al.Super-short-term forecast of solar irradiance based on CNN-Bi-LSTM[J]. Acta energiae solaris sinica, 2022, 43(3): 197-202.
[11] ALI M, PRASAD R, XIANG Y, et al.Variational mode decomposition based random forest model for solar radiation forecasting: new emerging machine learning technology[J]. Energy reports, 2021, 7: 6700-6717.
[12] KUMAR D S, YAGLI G M, KASHYAP M, et al.Solar irradiance resource and forecasting: a comprehensive review[J]. IET renewable power generation, 2020, 14(10): 1641-1656.
[13] KUMARI P, TOSHNIWAL D.Deep learning models for solar irradiance forecasting: a comprehensive review[J]. Journal of cleaner production, 2021, 318: 128566.
[14] DONG J, OLAMA M M, KURUGANTI T, et al.Novel stochastic methods to predict short-term solar radiation and photovoltaic power[J]. Renewable energy, 2020, 145: 333-346.
[15] RAZA M Q, NADARAJAH M, EKANAYAKE C.On recent advances in PV output power forecast[J]. Solar energy, 2016, 136: 125-144.
[16] 赵雅雪, 王旭, 蒋传文, 等. 基于最大信息系数相关性分析和改进多层级门控LSTM 的短期电价预测方法[J]. 中国电机工程学报, 2021, 41(1): 135-146, 404.
ZHAO Y X, WANG X, JIANG C W, et al.A novel short-term electricity price forecasting method based on correlation analysis with the maximal information coefficient and modified multi-hierachy gated LSTM[J]. Proceedings of the CSEE, 2021, 41(1): 135-146, 404.
[17] MARZOUQ M, EL FADILI H, ZENKOUAR K, et al.Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data[J]. Renewable energy, 2020, 157: 214-231.
[18] WANG F, LU X X, MEI S W, et al.A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant[J]. Energy, 2022, 238: 121946.
[19] RESHEF D N, RESHEF Y A, FINUCANE H K, et al.Detecting novel associations in large data sets[J]. Science, 2011, 334(6062): 1518-1524.
[20] 陈庄, 周籴. 基于MIC-XGBoost 算法的居民用水量数据预测[J]. 计算机应用与软件, 2021, 38(10): 125-130.
CHEN Z, ZHOU D.Prediction of residents’water consumption data based on MIC-XGBoost algorithm[J]. Computer applications and software, 2021, 38(10): 125-130.
[21] GUO Z, YU B, HAO M Y, et al.A novel hybrid method for flight departure delay prediction using random forest regression and maximal information coefficient[J]. Aerospace science and technology, 2021, 116: 106822.
[22] 侯宁, 张晓通, 魏瑜, 等. 基于随机森林方法的中国地表短波辐射估算[J]. 太阳能学报, 2021, 42(2): 31-36.
HOU N, ZHANG X T, WEI Y, et al.Estimation of surface incident shortwave radiation over China based on random forest regression method[J]. Acta energiae solaris sinica, 2021, 42(2): 31-36.
[23] 刘剑, 曹美燕, 高治军, 等. 一种基于随机森林的太阳能辐射预测模型[J]. 控制工程, 2017, 24(12): 2472-2477.
LIU J, CAO M Y, GAO Z J, et al.A solar radiation prediction model based on random forest[J]. Control engineering of China, 2017, 24(12): 2472-2477.
[24] 傅望安, 张泽发, 黄伟. 基于极端随机树的火电厂再热器故障预警算法研究[J]. 上海电力大学学报, 2020, 36(5): 445-450.
FU W A, ZHANG Z F, HUANG W.Research on fault early warning algorithm of reheater in thermal power plant based on extreme random tree[J]. Journal of Shanghai University of Electric Power, 2020, 36(5): 445-450.
[25] 陈宇韬, 唐明珠, 吴华伟, 等. 基于极端随机森林的大型风电机组发电机故障检测[J]. 湖南电力, 2019, 39(6): 45-51.
CHEN Y T, TANG M Z, WU H W, et al.Fault detection based on extreme random forest for large wind turbine generators[J]. Hunan electric power, 2019, 39(6): 45-51.
[26] 叶家豪, 魏霞, 黄德启, 等. 基于灰色关联分析的BSO-ELM-AdaBoost风电功率短期预测[J]. 太阳能学报, 2022, 43(3): 426-432.
YE J H, WEI X, HUANG D Q, et al.Short-term forecast of wind power based on BSO-ELM-AdaBoost with grey correlation analysis[J]. Acta energiae solaris sinica, 2022, 43(3): 426-432.
[27] 王琦, 季顺祥, 钱子伟, 等. 基于熵理论和改进ELM的光伏发电功率预测[J]. 太阳能学报, 2020, 41(10): 151-158.
WANG Q, JI S X, QIAN Z W, et al.Photovoltaic power prediction based on entropy theory and improved ELM[J]. Acta energiae solaris sinica, 2020, 41(10): 151-158.
[28] 胡兵, 王小娟, 徐立军, 等. 基于KMO-PCA-BP的燃料电池堆输出电压预测方法[J]. 太阳能学报, 2022, 43(3): 12-19.
HU B, WANG X J, XU L J, et al.Output voltage prediction method of fuel cell stack based on KMO-PCA-BP[J]. Acta energiae solaris sinica, 2022, 43(3): 12-19.
[29] BISOYI N, GUPTA H, PADHY N P, et al.Prediction of daily sediment discharge using a back propagation neural network training algorithm: a case study of the Narmada River, India[J]. International journal of sediment research, 2019, 34(2): 125-135.
[30] DU B, LUND P D, WANG J, et al.Comparative study of modelling the thermal efficiency of a novel straight through evacuated tube collector with MLR, SVR, BP and RBF methods[J]. Sustainable energy technologies and assessments, 2021, 44: 101029.
[31] LIMA M A F B, FERNÁNDEZ RAMÍREZ L M, CARVALHO P, et al. A comparison between deep learning and support vector regression techniques applied to solar forecast in spain[J]. Journal of solar energy engineering, 2022, 144(1): 010802.
[32] 徐炜君. 基于灰狼优化SVR的风电场功率超短期预测[J]. 杭州师范大学学报(自然科学版), 2021, 20(2): 177-182.
XU W J.Ultra short term wind power forecasting of wind farm based on grey wolf optimized SVR[J]. Journal of Hangzhou Normal University(natural science edition), 2021, 20(2): 177-182.

基金

国家重点研发计划(2018YFB1502902); 陕西省自然科学基金(2021JM-378; 2021JQ-493)

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