Version 1
: Received: 24 June 2024 / Approved: 25 June 2024 / Online: 25 June 2024 (16:26:51 CEST)
How to cite:
Li, D.; Qu, J.; Zhu, D.; Qin, Z.; Zhang, Q. Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine using WAO-XGBoost Model. Preprints2024, 2024061773. https://doi.org/10.20944/preprints202406.1773.v1
Li, D.; Qu, J.; Zhu, D.; Qin, Z.; Zhang, Q. Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine using WAO-XGBoost Model. Preprints 2024, 2024061773. https://doi.org/10.20944/preprints202406.1773.v1
Li, D.; Qu, J.; Zhu, D.; Qin, Z.; Zhang, Q. Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine using WAO-XGBoost Model. Preprints2024, 2024061773. https://doi.org/10.20944/preprints202406.1773.v1
APA Style
Li, D., Qu, J., Zhu, D., Qin, Z., & Zhang, Q. (2024). Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine using WAO-XGBoost Model. Preprints. https://doi.org/10.20944/preprints202406.1773.v1
Chicago/Turabian Style
Li, D., Zheyu Qin and Qiansu Zhang. 2024 "Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine using WAO-XGBoost Model" Preprints. https://doi.org/10.20944/preprints202406.1773.v1
Abstract
The solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the quantity of solar panels and the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to forecast the solar irradiance of typical irrigation areas. The impact of the meteorological parameters on the solar irradiance is studied, and four different parameter combinations are formed and considered as input to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict the solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as the input parameters. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model, with predicted values falling within acceptable ranges. The model developed using mixed data of multiple stations can be applied for forecasting solar irradiance in different regions. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.
Keywords
agricultural irrigation; mobile sprinkler machine; solar power generation; irradiance; machine learning; prediction model
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
