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首页> 外文期刊>Concurrency and computation: practice and experience >Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models
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Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models

机译：使用数值天气模型进行每日太阳能预测和插值的机器学习技术

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This article addresses two issues in solar energy forecasting from the numerical weather prediction (NWP) models using machine learning. First, we are interested in determining the relevant information for the forecasting task. With this purpose, a study has been carried out to evaluate the influence on accuracy of the number of NWP grid nodes used as input for the forecasting model, as well as their relative importance. Several machine learning (support vector machines and gradient boosting) and feature selection algorithms (linear, ReliefF, and local information analysis) have been used in this study. The second aim is to be able to predict solar energy for locations where no previous production data are available. To address this goal, an approach consisting on modeling regions in the grid is proposed. Models (aggregate models) use as input attributes the meteorological variables relevant for the region and two new inputs to identify the location of each station: the latitude and the longitude. Those models can be used to predict energy production for existing stations and for new locations, represented by latitude and longitude. Copyright © 2015 John Wiley & Sons, Ltd.
机译：本文讨论了使用机器学习从数值天气预报（NWP）模型进行的太阳能预报中的两个问题。首先，我们对确定预测任务的相关信息感兴趣。为此，已经进行了一项研究，以评估用作预测模型输入的NWP网格节点数量的准确性及其相对重要性，这对评估有影响。在这项研究中使用了几种机器学习（支持向量机和梯度提升）和特征选择算法（线性，ReliefF和局部信息分析）。第二个目标是能够预测没有先前生产数据的位置的太阳能。为了实现这个目标，提出了一种包括对网格中的区域进行建模的方法。模型（集合模型）使用与该地区相关的气象变量和两个新输入来标识每个气象站的位置作为输入属性：纬度和经度。这些模型可用于预测现有站点和新位置的能源生产，以纬度和经度表示。版权所有©2015 John Wiley＆Sons，Ltd.

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