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Assessment of different combinations of meteorological parameters for predicting daily global solar radiation using artificial neural networks

机译：使用人工神经网络评估气象参数的不同组合以预测每日的全球太阳辐射

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摘要

In this study, for determining the best-input scenarios of the used parameters in predicting the Daily Global Solar Radiation (DGSR), a new approach based on Artificial Neural Networks (ANNs) was presented. The proposed approach is based on comparisons between all possible input combinations for determining the best scenarios that can give perfect correlations and approximations with DGSR. Recorded data from 35 stations belonging to different climatic zones (27 in Morocco and 8 in neighboring countries) were reported for training and testing the obtained results. The used input parameters include geographical coordinates, sun declination, day length, day number, clearness index (KI), Top Of Atmosphere (TOA), average ambient temperature (T-a), maximum temperature (T-max), minimum temperature (T-min) difference temperature (Delta T), temperature ratio (F-R), relative humidity (Rh) and wind speed (Ws). The results revealed 128 best-input scenarios, where the first relevant input combination was found for KI, T-a, Delta T, T-R and TOA. This result indicated that the best-input scenario for predicting DGSR is based only on three climatological parameters: KI, function of Ta f(Ta) and TOA. In addition, based on these found best-input scenarios and on the least square regression (LSR) technique, 128 new linear relationships between DGSR and the found best-input combinations were developed. The statistical analysis expressed through statistical criteria indicated perfect correlations and approximations between the predicted and measured values of DGSR.
机译：在这项研究中，为了确定预测日全球太阳辐射量（DGSR）所用参数的最佳输入方案，提出了一种基于人工神经网络（ANN）的新方法。所提出的方法是基于所有可能的输入组合之间的比较，以确定可以提供与DGSR完美相关和近似的最佳方案。报告了来自不同气候区的35个台站（摩洛哥的27个和邻国的8个）的记录数据，用于培训和测试所获得的结果。使用的输入参数包括地理坐标，日偏角，天长，天数，净度指数（KI），大气层顶部（TOA），平均环境温度（Ta），最高温度（T-max），最低温度（T-最小）差温度（Delta T），温度比（FR），相对湿度（Rh）和风速（Ws）。结果揭示了128种最佳输入方案，其中找到了KI，T-a，Delta T，T-R和TOA的第一个相关输入组合。该结果表明，用于预测DGSR的最佳输入方案仅基于三个气候参数：KI，Ta f（Ta）和TOA的函数。此外，基于这些找到的最佳输入方案和最小二乘回归（LSR）技术，开发了DGSR与找到的最佳输入组合之间的128个新线性关系。通过统计标准表达的统计分析表明，DGSR的预测值和测量值之间具有完美的相关性和近似性。

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