[1]王 粟,隗磊锋,曾 亮. 基于PSO-RVM模型的光伏出力日前区间预测[J].湖北工业大学学报,2021,(5):37-41.
　WANG Su,WEI Leifeng,ZENG Liang. Day Ahead Interval Forecasting Model of PV Power Based on PSO-RVM[J].,2021,(5):37-41.
点击复制

 基于PSO-RVM模型的光伏出力日前区间预测()
分享到：

《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2021年第5期
页码:
37-41
栏目:
湖北工业大学学报
出版日期:
2021-10-31

文章信息/Info

Title:
 Day Ahead Interval Forecasting Model of PV Power Based on PSO-RVM
文章编号:
1003-4684(2021)05-0037-05
作者:
 王 粟;  隗磊锋;  曾 亮
 湖北工业大学电气与电子工程学院， 湖北 武汉 430068
Author(s):
 WANG Su;  WEI Leifeng;  ZENG Liang
 School of Electrical and Electronic Engin., Hubei Univ. of Tech.,Wuhan 430068,China
关键词:
 ; 光伏功率;  区间预测;  相关向量机;  粒子群算法;  混合核
Keywords:
 ; photovoltaic power;  interval prediction;  relevance vector machine;  particle swarm optimization;  hybrid kernel
分类号:
TM615
文献标志码:
A
摘要:
 针对确定性点功率预测不能准确反映出光伏功率的随机性和波动性的问题， 提出一种基于粒子群优化算法(PSO)，优化相关向量机(RVM)的光伏功率区间预测模型。通过关联系数选出待预测日的相似日；对于选出的相似日采用混合核RVM模型进行训练预测；为了增加模型的精确度，采用PSO算法对混合核RVM模型的参数进行优化。实验结果表明，该方法具有更高的精度和可靠的区间宽度。
Abstract:
 With the increase of photovoltaic grid connection, accurate photovoltaic power prediction is of great significance for the stable and safe operation of the grid. Deterministic power prediction cannot accurately reflect the randomness and volatility of photovoltaic power. Therefore, this paper proposes a photovoltaic power interval prediction model based on particle swarm optimization (PSO) optimization correlation vector machine (RVM). Firstly, the similar days of the days to be predicted are selected by the correlation coefficient; the hybrid kernel RVM model is used to train and predict the selected similar days; in order to increase the accuracy of the model, the PSO algorithm is used to optimize the parameters of the hybrid kernel RVM model. The experimental results show that the method proposed in this paper has higher accuracy and reliable interval width.

参考文献/References:

[1] ZHANG J, FLORITA A, HODGE B M, et al. A suite of metrics for assessing the performance of solar power forecasting[J]. Solar Energy, 2015, 111(01): 157-175.
[2] 荆博, 谭伦农, 钱政, 等.光伏发电短期预测研究进展综述[J]. 电测与仪表, 2017, 54(12): 1-6.
[3] QIANG NI, SHENGXIAN ZHUANG, HANMIN SHENG, et al. An optimized prediction intervals approach for short term PV power forecasting[J]. Energies 2017, 10, 1669,https://doi.org/10.3390/en10101669.
[4] HONGLU ZHU, WEIWEI LIAN, LINGXING LU, et al. An improved forecasting method for photovoltaic power based on adaptive bp neural network with a scrolling time window[J]. Energies 2017, 10, 1542, https://doi.org/10.3390/en10101542.
[5] 武小梅, 张琦, 田明正. 基于VMD-SE和优化支持向量机的光伏预测方法[J]. 电力科学与工程, 2017, 33(9): 29-36.
[6] 李国, 江晓东. 基于提升回归树与随机森林的风电功率集成预测方法[J]. 电力系统及其自动化学报, 2018, 30(11): 70-74.
[7] 陈瑶,陈晓宁.基于自适应Kmeans和LSTM的短期光伏发电预测[J/OL].电测与仪表:1-5[2021-10-07].http://kns.cnki.net/kcms/detail/23.1202.TH.20200521.1438.014.html.
[8] SEN WANG, YONGHUI SUN, YAN ZHOU, et al. A new hybrid short-term interval forecasting of PV output power based on EEMD-SE-RVM[J]. Energies 2020, 13, 87, https://doi.org/10.3390/en13010087.
[9] QIANG NI, SHENGXIAN ZHUANG, HANMIN SHENG,et al.An ensemble prediction intervals approach for short-term PV power forecasting[J]. Solar Energy，2017，155:1072-1083.
[10] 孙国强, 梁智, 俞娜燕, 等. 基于EWT和分位数回归森林的短期风电功率概率密度预测[J]. 电力自动化设备, 2018, 38(8): 158-165.
[11] 许佳辉,王向文,杨俊杰.基于CNN-QRLightGBM的短期负荷概率密度预测[J].电网技术,2020,44(9):3409-3416.
[12] 陈通, 孙国强, 卫志农, 等. 基于相似日和CAPSO-SNN的光伏发电功率预测[J]. 电力自动化设备, 2017, 37(3): 66-71.
[13] JIA DING, MAOLIN WANG, ZUOWEI PING, et al. An integrated method based on relevance vector machine for short-term load forecasting[J]. European Journal of Operational Research，2020， 287： 497-510\.
[14] 王东, 朱永利. 基于模糊C均值聚类和改进相关向量机的变压器故障诊断[J]. 电测与仪表, 2019, 56(12): 14-19.
[15] ESEYE A T, ZHANG J, ZHENG D. Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information[J]. Renewable Energy, 2017, 118(4): 357-367.

相似文献/References:

[1]熊韧,曹海印,王焱清,等.非牛顿润滑静压轴承的节流器流量方程修正[J].湖北工业大学学报,2019,34(5):6.
　XIONG Ren,CAO Haiyin,WANG Yanqing,et al.Modified restrictor flow equations of hydrostatic bearings ubricated by non-Newtonian fluids[J].,2019,34(5):6.
[2]王照远,曹 民,王 毅,等. 场景与数据双驱动的隧道图像拼接方法[J].湖北工业大学学报,2020,(4):11.
　WANG Zhaoyuan,CAO Min,WANG Yi,et al. Tunnel Image Stitching Method based on Scene and Data[J].,2020,(5):11.
[3]潘 健,梁佳成,陈凤娇,等. 单电流闭环多重PR控制的LCL型逆变器[J].湖北工业大学学报,2020,(4):16.
　PAN Jian,LIANG Jiacheng,CHEN Fengjiao,et al. Design of LCL Grid Connected Inverter based on Single Closed Loop Control and Multiple PR Controllers[J].,2020,(5):16.
[4]王晓光,赵 萌,文益雪,等. 定子闭口槽结构对永磁电机齿槽转矩影响分析[J].湖北工业大学学报,2020,(4):25.
　WANG Xiaoguang,ZHAO Meng,WEN Yixue,et al. Study on Cogging Torque and Vibration Noise of Permanent Magnet Motor with Segmental Stator and Closed-Slot[J].,2020,(5):25.
[5]宇 卫,凃玲英,陈 健. 风电场集中接入对集电线电流保护的影响[J].湖北工业大学学报,2020,(4):29.
　YU Wei,TU Lingying,CHEN Jian. Effect of the Collective Line Current Protection when Wind Farms are Centralized Accessed to the Power System[J].,2020,(5):29.
[6]廖政斌,王泽飞,祝 珊. 二惯量系统谐振在线抑制及相位补偿[J].湖北工业大学学报,2020,(4):34.
　LIAO Zhengbin,WANG Zefei,ZHU Shan. Online Resonance Suppression and Phase Compensation for Double Inertia System[J].,2020,(5):34.
[7]王 欣,游 颖,姜天翔,等. 面向3D打印过程的产品工艺设计和优化[J].湖北工业大学学报,2020,(4):39.
　WANG Xin,YOU Ying,JIANG Tianxiang,et al. Product Process Design and Optimization for 3D Printing Processes[J].,2020,(5):39.
[8]冉晶晶,文 红,罗雅梅,等. 全自动样品前处理平台及其控制系统[J].湖北工业大学学报,2020,(4):43.
　RAN Jingjing,WEN Hong,LUO Yamei,et al. Research on Automatic Sample Preprocessing Platform and its Control System[J].,2020,(5):43.
[9]杨 磊,马志艳,石 敏,等. 基于模糊PID的小型冷库过热度控制方法[J].湖北工业大学学报,2020,(4):43.
　YANG Lei,MA Zhiyan,SHI Min,et al. Research on Superheat Control Method of Small Cold Storage based on Fuzzy PID[J].,2020,(5):43.
[10]黄 晶,周细枝,周业望. 动态注塑成型模具的设计与实验研究[J].湖北工业大学学报,2020,(4):52.
　HUANG Jing,ZHOU Xizhi,ZHOU Yewang. Design and Experimental Study of Dynamic Injection Molding[J].,2020,(5):52.

备注/Memo

备注/Memo:
 ［收稿日期］ 2020-03-15
［第一作者］ 王 粟(1964-)，女，湖北松滋人，湖北工业大学教授，研究方向为智能控制算法
［通信作者］ 曾 亮(1980-)，男，湖南临湘人，湖北工业大学教授，研究方向为机器视觉与人工智能理论
更新日期/Last Update: 2021-11-01