利用机器学习算法确定影响奎松市光伏太阳能发电的气象参数

Q3 Multidisciplinary
Lea Angela Saure, Joshua Quides, Raymond C. Ordinario, Rhenish C. Simon
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引用次数: 0

摘要

适应太阳能光伏(PV)模块发电的一个挑战是其随天气条件变化的可变性。在本研究中,我们旨在确定对太阳能发电(SEG)变率影响最大的气象参数的影响。我们的研究是在奎松市进行的,奎松市是菲律宾国家首都区的一部分。在基于主成分回归(PCR)和随机森林回归(RFR)机器学习算法的研究中,我们考虑的8个气象参数中,最高温度、相对湿度、人的温度和云的不透明度对SEG的变异性影响最大。PCR模型分别解释了训练集和测试集SEG的55.5%和49.2%的变异性。另一方面,RFR模型在训练集中解释了77.1%的SEG变化,在测试集中解释了52.7%。此外,这两个模型提供了可比较的SEG预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determining Meteorological Parameters Influencing Photovoltaic Solar Energy Generation in Quezon City Using Machine Learning Algorithms
One challenge in adapting to energy generation using solar photovoltaic (PV) modules is its variability with changing weather conditions. In this study, we aim to determine the effect of meteorological parameters that have the most effect on the variability of solar energy generation (SEG). Our study is conducted in Quezon City, part of the National Capital Region, Philippines. The maximum temperature, relative humidity, man temperature, and cloud opacity have the most effect on the variability of the SEG among the eight meteorological parameters that we consider in our study based on the principal component regressor (PCR) and random forest regressor (RFR) machine learning algorithms. The PCR model explains 55.5 and 49.2% variability in SEG of the training and test sets, respectively. On the other hand, the RFR model explains a 77.1% variation of the SEG in the training and 52.7% in the test set. Furthermore, the two models provided comparable predictions of SEG.
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来源期刊
Philippine Journal of Science
Philippine Journal of Science Multidisciplinary-Multidisciplinary
CiteScore
1.20
自引率
0.00%
发文量
55
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