新闻
新闻资讯
联系我们
联系人:陈先生
手机:13888889999
电话:020-88888888
邮箱:youweb@126.com
地址:广东省广州市番禺经济开发区
行业资讯
PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON IGWO-LSTM
作者:佚名 发布时间:2024-05-20 18:58:05
[1] 彭青龙. 绿色发展、科技人文与跨学科思维——访谈金涌院士[J]. 上海交通大学学报(哲学社会科学版), 2021, 29(4): 1-11. PENG Q L.Green development, techno-humanities, and interdisciplinary thinking: an interview with Jin Yong[J]. Journal of Shanghai Jiao Tong University(philosophy and social sciences), 2021, 29(4): 1-11. [2] 胡雪凯, 时珉, 胡文平, 等. 光伏电站功率预测影响因素分析及准确率提升方法研究[J]. 河北电力技术, 2020, 39(2): 1-6. HU X K, SHI M, HU W P, et al.Study on power forecast affecting factors of photovoltaic power plant and method of improving accuracy rate[J]. Hebei electric power, 2020, 39(2): 1-6. [3] WANG K J, QI X X, LIU H D.Photovoltaic power forecasting based LSTM-Convolutional Network[J]. Energy, 2019, 189: 116225. [4] LOPES F M, SILVA H G, SALGADO R, et al.Short-term forecasts of GHI and DNI for solar energy systems operation: assessment of the ECMWF integrated forecasting system in southern Portugal[J]. Solar energy, 2018, 170: 14-30. [5] MILLER S D, ROGERS M A, HAYNES J M, et al.Short-term solar irradiance forecasting via satellite/model coupling[J]. Solar energy, 2018, 168: 102-117. [6] SANjARI M J, GOOI H B. Probabilistic forecast of PV power generation based on higher order Markov chain[J]. IEEE transactions on power systems, 2016, 32(4): 2942-2952. [7] YONA A, SENJYU T, FUNABASHI T, et al.Determination method of insolation prediction with fuzzy and applying neural network for long-term ahead PV power output correction[J]. IEEE transactions on sustainable energy, 2013, 4(2): 527-533. [8] BHAVSAR S, PITCHUMANI R, ORTEGA-VAZQUEZ M A. Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts[J]. Applied energy, 2021, 293: 116964. [9] FERLITO S, ADINOLFI G, GRADITI G.Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production[J]. Applied energy, 2017, 205: 116-129. [10] LIU J, FANG W L, ZHANG X D, et al.An improved photovoltaic power forecasting model with the assistance of aerosol index data[J]. IEEE transactions on sustainable energy, 2015, 6(2): 434-442. [11] BOUZERDOUM M, MELLIT A, PAVAN A M.A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant[J]. Solar energy, 2013, 98(4): 226-235. [12] 张雨金, 杨凌帆, 葛双冶, 等. 基于Kmeans-SVM 的短期光伏发电功率预测[J]. 电力系统保护与控制, 2018, 46(21): 118-124. ZHANG Y J, YANG L F, GE S Y, et al.Short-term photovoltaic power forecasting based on Kmeans algorithm and support vector machine[J]. Power system protection and control, 2018, 46(21): 118-124. [13] 殷豪, 陈云龙, 孟安波, 等. 基于二次自适应支持向量机的光伏输出功率预测[J]. 太阳能学报, 2019, 40(7): 1866-1873. YIN H, CHEN Y L, MENG A B, et al.Forecasting photovoltaic power based on quadric self-adaptive svm model[J]. Acta energiae solaris sinica, 2019, 40(7): 1866-1873. [14] ESEYE A T, ZHANG J H, ZHENG D H.Short-term photovoltaic solar power forecasting using a hybrid wavelet PSO-SVM model based on SCADA and meteorological information[J]. Renewable energy, 2018, 118: 357-367. [15] 江志晃. 一种面对大数据集的改进基于支持向量机的算法性能分析[J]. 自动化技术与应用, 2020, 39(2): 27-29. JIANG Z H.An improved algorithm performance analysis based on support vector machine(SVM)for large data sets[J]. Techniques of automation and applications, 2020, 39(2): 27-29. [16] 王育飞, 付玉超, 薛花. 基于Chaos-EEMD-PFBD 分解和 GA-BP 神经网络的光伏发电功率超短期预测法[J]. 太阳能学报, 2020, 41(12): 55-62. WANG Y F, FU Y C, XUE H.Ultra-short-term forecasting method of photovoltaic power generation based on chaos-EEMD-PFBD decomposition and GA-BP neural networks[J]. Acta energiae solaris sinica, 2020, 41(12): 55-62. [17] 丁坤, 丁汉祥, 王越, 等. 基于提升小波-BP 神经网络的光伏阵列短期功率预测[J]. 可再生能源, 2017, 35(4): 566-571. DING K, DING H X, WANG Y, et al.Short-term power prediction of photovoltaic array based on lifting wavelet transform-BP neural network[J]. Renewable energy resources, 2017, 35(4): 566-571. [18] 卢忠山, 袁建华. 基于EEMD-LSTM方法的光伏发电系统超短期功率预测[J]. 中国测试,2022, 48(12): 125-132. LU Z S, YUAN J H.Ultra-short term power prediction of photovoltaic power generation system based on EEMD-LSTM method[J]. China measurement & test,2022, 48(12): 125-132. [19] 张晓凤, 王秀英. 灰狼优化算法研究综述[J]. 计算机科学, 2019, 46(3): 30-38. ZHANG X F, WANG X Y.Comprehensive review of grey wolf optimization algorithm[J]. Computer science, 2019, 46(3): 30-38. [20] 孙辉, 邓志诚, 赵嘉, 等. 混合均值中心反向学习粒子群优化算法[J]. 电子学报, 2019, 47(9): 1809-1818. SUN H, DENG Z C, ZHAO J, et al.Hybrid mean center opposition-based learning particle swarm optimization[J]. Acta electronica sinica, 2019, 47(9): 1809-1818. [21] 杨晶显, 张帅, 刘继春, 等. 基于VMD和双重注意力机制LSTM的短期光伏功率预测[J]. 电力系统自动化, 2021, 45(3):174-182. YANG J X, ZHANG S, LIU J C, et al.Short-term photovoltaic power prediction based on variational mode decomposition and long short term memory with dual-stage attention mechanism[J]. Automation of electric power systems, 2021, 45(3): 174-182. |
新闻资讯
-
2024-07-01 13:15:52
为什么删掉了社交媒体?约基奇:我觉得那
-
2024-07-01 13:15:36
7个免费下载TXT小说的网站,97%的小说资源都能找到
-
2024-07-01 13:15:13
抖音极速版19.9下载
-
2024-07-01 13:14:53
机器学习|model.compile()用法
-
2024-07-01 13:14:15
泾阳秦川乳业分享纯牛奶的营养价值,喝纯牛奶的好处与功效
-
2024-07-01 13:13:34
氮化镓GaN快充,PD快充,普通的快充,有什么区别?同一个概念吗?