A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran

作者：Nazhad, Seyed Hossein Hosseini; Lotfinejad, Mohammad Mehdi; Danesh, Malihe; ul Amin, Rooh; Shamshirband, Shahaboddin^*

来源：International Journal of Remote Sensing, 2017, 38(23): 6894-6909.

DOI：10.1080/01431161.2017.1368098

• ARTIFICIAL NEURAL-NETWORK
• FEEDFORWARD NETWORKS
• AQUIFER PARAMETERS
• SKY CONDITION
• ENERGY
• OPTIMIZATION
• ALGORITHM
• SUNSHINE
• INDIA

This study uses the empirical models of extreme learning machine (ELM) method to predict daily horizontal diffuse solar radiation (HDSR). As a possibility for modification, the recent hybrid ELM methods such as complex ELM (C-ELM), self-adaptive evolutionary ELM (SaE-ELM), and online sequential ELM (OS-ELM) have been developed for the prediction of the daily HDSR. The empirical model of ELM predicts the HDSR using clearness index as the sole predictor. For this aim, two types of correlations are evaluated: (1) the diffuse fraction-clearness index and (2) the diffuse coefficient-clearness index. The measured diffuse and global solar radiation data sets of southern Iranian cities (Yazd, Shiraz, Bandar Abbas, Bushehr, and Zahedan) are utilized to evaluate the models. The precision of the C-ELM, SaE-ELM, OS-ELM, and ELM models is evaluated for different regions on the basis of five statistical performance evaluation parameters. The results confirm that the performance of hybrid ELM is pretty accurate and trustworthy. Fully complex ELM exhibits the best performance among these hybrid methods, having values of 0.87 and 0.30 for R-2 and RMSE measures, respectively, in the testing phase. Therefore, it is able to be recognized as an appropriate tool for daily solar radiation forecasting issues.

• 出版日期2017
• 单位西北工业大学