Photovoltaic power generation prediction based on data mining and genetic wavelet neural network

被引：0
|
作者：
Zhang C. [1 ]
Bai J. [1 ]
Lan K. [1 ]
Huan X. [1 ]
Fan C. [1 ]
Xia X. [1 ]
机构：
[1] College of Mechanical and Electrical Engineering, Hohai University, Changzhou
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2021年 / 42卷 / 03期
关键词：
Cluster analysis; Data mining; Genetic wavelet neural network; PV power generation; Wavelet analysis;
D O I：
10.19912/j.0254-0096.tynxb.2019-0867
中图分类号：
学科分类号：
摘要：
In order to solve the prediction uncertainty and further improve the prediction accuracy for photovoltaic (PV) power stations. The paper proposes a hybrid prediction model for forecasting the generation of PV power stations based on data mining and genetic wavelet neural network. The model utilizes the K mean clustering algorithm to classify historical data and constructs a genetic wavelet neural network based on BP neural network by using wavelet analysis. Furthermore, the initial parameters of the network can be globally optimized with a genetic algorithm and the learning rules of the network are improved by a cross-entropy function. The proposed network has not only good characteristics with local time and frequency domain owned by wavelet analysis, but also has global search ability, which increases the possibility of jumping out of local optimum and has faster convergence and better stability. The experimental results shown the effectiveness of the proposed method. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：375 / 382
页数：7
相关论文
共 14 条
  • [1] RAZA M Q, NADARAJAH M, EKANAYAKE C., On recent advances in PV output power forecast, Solar energy, 136, pp. 125-144, (2016)
  • [2] HABLE M, MEISENBACH C, WINKLER G., Economically optimised power dispatch in local systems using evolutionary algorithms and dynamic programming, Fifth International Conference on Power System Management and Control, pp. 174-179, (2002)
  • [3] ALMONACID F, PeREZ-HIGUERAS P J, FERNaNDEZ E F, Et al., A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator, Energy conversion & management, 85, 9, pp. 389-398, (2014)
  • [4] KEMMOKU Y, ORITA S, NAKAGAWA S, Et al., Daily insolation forecasting using a multi-stage neural network, Solar energy, 66, 3, pp. 193-199, (1999)
  • [5] MUELLER R, DAGESTAD K F, INEICHENP, Et al., Rethinking satellite-based solar irradiance modelling: The SOLIS clear-sky module, Remote sensing of environment, 91, 2, pp. 160-174, (2004)
  • [6] CAO J C, CAO S H., Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis, Energy, 31, 15, pp. 3435-3445, (2006)
  • [7] CHEN C S, DUAN S X, YIN J J., Design of photovoltaic array power generation prediction model based on neural network, Transactions of China Electrotechnical Society, 24, 9, pp. 153-158, (2009)
  • [8] MORI H, TAKAHASHI A., A data mining method for selecting input variables for forecasting model of global solar radiation, IEEE PES Transmission and Distribution Conference and Exposition, (2012)
  • [9] SHARMA V, YANG D, WALSH W, Et al., Short term solar irradiance forecasting using a mixed wavelet neural network, Renewable energy, 90, pp. 481-492, (2016)
  • [10] WANG J D, RAN R, SONG Z L, Et al., Short-term photovoltaic power generation forecasting based on environmental factors and GA-SVM, Journal of electrical engineering & technology, 12, 1, pp. 64-71, (2017)
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