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A novel Grouping Genetic Algorithm-Extreme Learning Machine approach for global solar radiation prediction from numerical weather models inputs

机译：从数值天气模型输入预测全球太阳辐射的新颖分组遗传算法-极限学习机方法

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This paper presents a novel scheme for global solar radiation prediction, based on a hybrid neural-genetic algorithm. Specifically a grouping genetic algorithm (GGA) and an Extreme Learning Machine algorithm (ELM) have been merged in a single algorithm, in such a way that the GGA solves the optimal selection of features, and the ELM carries out the prediction. The proposed scheme is also novel because it uses as input of the system the output of a numerical weather meso-scale model (WRF), i.e., atmospherical variables predicted by the WRF at different nodes. We consider then different problems associated with this general algorithmic framework: first, we evaluate the capacity of the GGA-ELM for carrying out a statistical downscaling of the WRF to a given point of interest (where a measure of solar radiation is available), i.e., we only take into account predictive variables from the WRF and the objective variable at the same time tag. In a second evaluation approach, we try to predict the solar radiation at the point of interest at different time tags t + x, using predictive variables from the WRF. Finally, we tackle the complete prediction problem by including previous values of measured solar radiation in the prediction. The proposed algorithm and its efficiency for selecting the best set of features from the WRF are analyzed in this paper, and we also describe different operators and dynamics for the GGA. Finally, we evaluate the performance of the system with these different characteristics in a real problem of solar radiation prediction at Toledo's radiometric observatory (Spain), where the proposed system has shown an excellent performance in all the subproblems considered, in terms of different error metrics. (C) 2016 Elsevier Ltd. All rights reserved.
机译：本文提出了一种基于混合神经遗传算法的全球太阳辐射预测新方案。具体而言，已将分组遗传算法（GGA）和极限学习机算法（ELM）合并到单个算法中，以使GGA解决特征的最佳选择，然后ELM进行预测。提议的方案也是新颖的，因为它使用数值气象中尺度模型（WRF）的输出作为系统的输入，即，WRF在不同节点处预测的大气变量。然后，我们考虑与该通用算法框架相关的不同问题：首先，我们评估GGA-ELM进行WRF的统计缩减规模到给定兴趣点（可利用太阳辐射的度量）的能力，即，我们只考虑来自WRF的预测变量和同时存在的目标变量。在第二种评估方法中，我们尝试使用WRF的预测变量来预测不同时间标签t + x处关注点的太阳辐射。最后，我们通过在预测中包括先前测量的太阳辐射值来解决完整的预测问题。本文分析了所提出的算法及其从WRF中选择最佳特征集的效率，并且我们还描述了GGA的不同算子和动力学。最后，我们在托莱多辐射观测站（西班牙）的太阳辐射预测的实际问题中评估了具有这些不同特征的系统的性能，该系统在考虑的所有子问题上，根据不同的误差度量，均显示出出色的性能。 （C）2016 Elsevier Ltd.保留所有权利。

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