Addressing the inefficiency of existing wind power data cleaning algorithms
a data cleaning method based on the raindrop erosion algorithm is proposed. This algorithm effectively removes outliers from wind power data by simulating the impact and erosion of raindrops on terrain. Experimental results indicate that the correlation coefficient between wind speed and power using the raindrop erosion algorithm reaches 0.977
demonstrating significant effectiveness in reducing data dispersion with a runtime of 2.1 seconds. Additionally
after cleaning the data using the raindrop erosion algorithm
the optimal wind power curve for a single wind turbine is fitted using a support vector regression (SVR) model with Bayesian optimization
from which the scheduled surplus electricity is calculated. Subsequently
simulation models of proton exchange membrane (PEM) electrolyzers and alkaline water (ALK) electrolyzers are used for comparative analysis of electricity consumption for hydrogen production utilizing surplus electricity. Simulation results show that the electricity consumption is 39.4 kW·h/kg for the PEM electrolyzer and 53.9 kW·h/kg for the ALK electrolyzer
suggesting that the PEM electrolyzer is more suitable for hydrogen production from surplus electricity.
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references
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