Research on New Energy Probability Prediction Technology Based on Ensemble Weather Forecast

被引:1
作者
Wu, Han [1 ]
Yuan, Yang [1 ]
Yu, Wei [2 ]
Wu, Chao [3 ]
Huang, Hao [2 ]
Dong, Annan [1 ]
机构
[1] State Power Rixin Technol Co Ltd, Dept Power Grid Business, Beijing, Peoples R China
[2] State Grid Zhejiang Zhoushan Power Supply Co, Dept Dispatching Ctr, Zhoushan, Peoples R China
[3] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai, Peoples R China
来源
2022 12TH INTERNATIONAL CONFERENCE ON POWER AND ENERGY SYSTEMS, ICPES | 2022年
关键词
new energy; power forecast; probability forecast; ensemble weather forecast;
D O I
10.1109/ICPES56491.2022.10072489
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In this paper, a ensemble weather forecasting method based on multi-initial value, multi-mode and multi-physical process, with multiple algorithm perturbations, and BMA+ EMOS statistical modeling is proposed. Based on the ensemble weather forecasting method, a new energy probabilistic power prediction method combining taboo algorithm and BP neural network algorithm is proposed. Through the design example analysis, the proposed method can effectively reduce the prediction bias, reduce the upper and lower limit bandwidth of probability prediction by 25%, and overcome the power distribution fat tail and multimodal anomalies, making the power prediction results more stable and accurate.
引用
收藏
页码:886 / 889
页数:4
相关论文
共 16 条
[1]   Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks [J].
Alharbi, Fahad Radhi ;
Csala, Denes .
ENERGIES, 2021, 14 (20)
[2]  
[Anonymous], 2010, APP CAL REN EN FOR R
[3]  
Hagan K.E., 2016, 2016 IEEE POWER ENER, P1
[4]  
Hamid R, 2022, IEEE T IND INFORM, P1
[5]   Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks [J].
Li, Dan ;
Jiang, Fuxin ;
Chen, Min ;
Qian, Tao .
ENERGY, 2022, 238
[6]  
Li Mingjie, 2022, CELL CYCLE, P1, DOI DOI 10.1080/15384101.2022.2051293
[7]   Probabilistic power flow with correlated wind sources [J].
Morales, J. M. ;
Baringo, L. ;
Conejo, A. J. ;
Minguez, R. .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2010, 4 (05) :641-651
[8]  
Parul A, 2021, IEEE T IND INFORM, P1
[9]   From Probabilistic Forecasts to Statistical Scenarios of Short-term Wind Power Production [J].
Pinson, Pierre ;
Madsen, Henrik ;
Nielsen, Henrik Aa. ;
Papaefthymiou, George ;
Kloeckl, Bernd .
WIND ENERGY, 2009, 12 (01) :51-62
[10]   An Adaptive Ensemble Data Driven Approach for Nonparametric Probabilistic Forecasting of Electricity Load [J].
Wan, Can ;
Cao, Zhaojing ;
Lee, Wei-Jen ;
Song, Yonghua ;
Ju, Ping .
IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (06) :5396-5408