Optimization of Power Prediction of BP Network with Improved Pelican Algorithm

Author:

Hui Lichuan,Li Yao,Zan Lizhi

Abstract

Abstract In view of the large fluctuation of photovoltaic output power affected by different weather, accurate prediction of photovoltaic output power is particularly important for the safe and stable operation of power system. Firstly, the pelican optimization algorithm ( POA ) is improved in the following three aspects : adding Circle chaotic map to make the population evenly distributed, introducing mutation factor to expand the search range of pelican when approaching prey, adding adaptive weight and firefly disturbance to avoid falling into local optimum in the water surface flight stage ; then, in order to improve the prediction accuracy of BP algorithm, the improved pelican algorithm ( IPOA ) is used to optimize the weights and thresholds of BP neural network, and the IPOA-BP photovoltaic power prediction model is built to improve the accuracy of power prediction. Finally, this paper tests the prediction performance of IPOA-BP, POA-BP and basic BP power prediction models in sunny, cloudy and rainy days through experiments. The experimental results demonstrate that the IPOA-BP prediction model outperforms both the POA-BP and traditional BP neural network models under various weather conditions.

Publisher

IOP Publishing

Reference16 articles.

1. Climate change impact on photovoltaic power potential in South America;Narvaez;Environmental Research Communications,2023

2. A CMIP6 assessment of the potential climate change impacts on solar photovoltaic energy and its atmospheric drivers in West Africa;Kwadwo;Environmental Research Letters,2022

3. Short-term photovoltaic power prediction method based on CatBoost algorithm;Chen;Zhejiang Electric Power,2023

4. Prediction of photovoltaic microgrid power generation based on wavelet neural network of genetic algorithm;Liu;Electrical Measurement & Instrumentation,2017

5. Short-term power generation prediction of photovoltaic power station based on BP neural network;Mo,2022

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