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首页> 外文期刊>Renewable Power Generation, IET >Evaluation and forecasting of solar radiation using time series adaptive neuro-fuzzy inference system: Seoul city as a case study
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Evaluation and forecasting of solar radiation using time series adaptive neuro-fuzzy inference system: Seoul city as a case study

机译：基于时间序列自适应神经模糊推理系统的太阳辐射评估与预报：以首尔市为例

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摘要

Given that solar radiation is unpredictable, an accurate solar energy prediction model must be developed. This study aimed to evaluate the changes in solar radiation over the past 37 years in Seoul city. The capability of the adaptive neuro-fuzzy inference system (ANFIS) to forecast solar radiation using chaotic time series inputs was analysed. Results demonstrate the capability of ANFIS to provide a relatively good monthly solar prediction model with a normalised root mean square error of 0.22%, a root mean square error of 55.4, and a coefficient of determination of 0.8. The Jarque-Bera test was implemented as well to test the null hypothesis for the normal distribution of standardised residual. Results support the null hypothesis with P-value = 0.222, which indicates the normal distribution of the standardised residual and its goodness. The standardised residual shows that the model can effectively predict solar radiation on a monthly basis.
机译：鉴于太阳辐射是不可预测的，因此必须开发出准确的太阳能预测模型。这项研究旨在评估首尔市过去37年中太阳辐射的变化。分析了自适应神经模糊推理系统（ANFIS）使用混沌时间序列输入预测太阳辐射的能力。结果表明，ANFIS能够提供相对较好的月度太阳预测模型，其归一化均方根误差为0.22％，均方根误差为55.4，确定系数为0.8。还执行了Jarque-Bera检验来检验标准化残差的正态分布的原假设。结果支持P值= 0.222的原假设，这表明标准化残差的正态分布及其优劣。标准化残差表明该模型可以有效地每月预测太阳辐射。

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