SHORT-TERM PV POWER PREDICTION BASED ON AMBOA-DBN COMBINED WITH SIMILAR DAYS

被引：0
作者：
Zhang C. [1 ,2 ]
Lin G. [1 ]
Huang J. [1 ,2 ]
Kuang Y. [1 ]
Liu J. [1 ]
机构：
[1] School of Electronic Electrical and Physics, Fujian University of Technology, Fuzhou
[2] Fujian Provincial University Engineering Research Center for Simulation Analysis and Integrated Control of Smart Grid, Fujian University of Technology, Fuzhou
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 06期
关键词：
adaptive algorithm; comprehensive grey correlation theory; data mining; deep learning; forecasting; photovoltaic power;
D O I：
10.19912/j.0254-0096.tynxb.2022-0275
中图分类号：
学科分类号：
摘要：
A data mining method based on grey relational theory was proposed to select similar days，and adaptive dynamic weight variable bat algorithm was used to optimize the parameters of DBN neural network. Firstly，the main factors affecting photovoltaic power generation were analyzed from two aspects of historical data set and predicted date data. On the basis of the original fuzzy grey correlation analysis，the comprehensive grey correlation theory was introduced to calculate the similarity degree of development trend of various attributes of things as the measurement standard to select the similarity day with higher similarity degree. The weight parameters of DBN were optimized by adaptive dynamic weighted bat algorithm in order to improve the neural network training process due to the improper selection of initial weight into local optimal or convergence time is too long. A short-term photovoltaic power prediction model is established. Compared with other prediction models，the experimental results show that this model is more accurate in prediction. © 2023 Science Press. All rights reserved.
引用
收藏
页码：290 / 299
页数：9
相关论文
共 24 条
  • [1] CHEN G P，, LI M J, Et al., Study on technical bottleneck of new energy development[J], Proceedings of the CSEE, 37, 1, pp. 20-27, (2017)
  • [2] ZHAO D Y，, HU N，, FU J，, Et al., Research on the practice and road map of enhancing the flexibility of a new generation power system in China[J], Power system protection and control, 48, 24, pp. 1-8, (2020)
  • [3] JIANG H, DONG Y., Forecast of hourly global horizontal irradiance based on structured Kernel Support Vector Machine：a case study of Tibet area in China[J], Energy conversion and management, 142, pp. 307-321, (2017)
  • [4] KOHLER C，, STEINER A, SAINT-DRENAN Y-M, Et al., Critical weather situations for renewable energies-Part B：low stratus risk for solar power[J], Renewable energy, 101, pp. 794-803, (2017)
  • [5] LI J W，, JIAO H, LIU F W，, Et al., Short-time segmented photovoltaic output forecasting based on similar period[J], Electric power automation equipment, 38, 8, pp. 183-188, (2018)
  • [6] CHEN Z, ZONG P P., PV output forecast based on grey correlation analysis with expanded sample[J], Acta energiae solaris sinica, 38, 11, pp. 2909-2915, (2017)
  • [7] YUAN T H，, ZHU N，, SHI Y F，, Et al., Sample data selection method for improving the prediction accuracy of the heating energy consumption[J], Energy and buildings, 158, pp. 234-243, (2018)
  • [8] GENG B, GAO Z Y, BAI H Y，, Et al., PV generation forecasting combined with similar days and GA-BP neural network[J], Proceedings of the CSU-EPSA, 29, 6, pp. 118-123, (2017)
  • [9] MENG A B, CHEN J M, LI Z L, Et al., Short-term photovoltaic power generation prediction based on similar day theory and CSO-WGPR[J], High voltage engineering, 47, 4, pp. 1176-1184, (2021)
  • [10] WU Y, BAO L S，, Et al., Short-term load forecasting based on improved grey relational analysis and neural network optimized by bat algorithm[J], Automation of electric power systems, 42, 20, pp. 67-72, (2018)
123