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

Zhang Cheng; Lin Guqing; Huang Jing; Kuang Yu; Liu Jiajing

doi:10.19912/j.0254-0096.tynxb.2022-0275

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (6) : 290-299. DOI: 10.19912/j.0254-0096.tynxb.2022-0275

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

Zhang Cheng^1,2, Lin Guqing¹, Huang Jing^1,2, Kuang Yu¹, Liu Jiajing¹

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Abstract

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.

Key words

data mining / deep learning / forecasting / photovoltaic power / adaptive algorithm / comprehensive grey correlation theory

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Zhang Cheng, Lin Guqing, Huang Jing, Kuang Yu, Liu Jiajing. SHORT-TERM PV POWER PREDICTION BASED ON AMBOA-DBN COMBINED WITH SIMILAR DAYS[J]. Acta Energiae Solaris Sinica. 2023, 44(6): 290-299 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0275

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