Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability

Fouilloy Alexis; Voyant Cyril; Notton Gilles; Motte Fabrice; Paoli Christophe; Nivet Marie-Laure; Guillot Emmanuel; Duchaud Jean-Laurent

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Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability

机译：基于机器学习的太阳辐射预测：取决于天气变化的预测模型选择方法

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Eleven statistical and machine learning tools are analyzed and applied to hourly solar irradiation forecasting for time horizon from 1 to 6 h. A methodology is presented to select the best and most reliable forecasting model according to the meteorological variability of the site. To make the conclusions more universal, solar data collected in three sites with low, medium and high meteorological variabilities are used: Ajaccio, Tilos and Odeillo. The datasets variability is evaluated using the mean absolute log return value. The models were compared in term of normalized root mean square error, mean absolute error and skill score. The most efficient models are selected for each variability and temporal horizon: for the weak variability, auto-regressive moving average and multi-layer perceptron are the most efficient, for a medium variability, auto-regressive moving average and bagged regression tree are the best predictors and for a high one, only more complex methods can be used efficiently, bagged regression tree and the random forest approach. (C) 2018 Elsevier Ltd. All rights reserved.

机译：分析了11种统计和机器学习工具，并将其应用于1到6小时的时间范围内的每小时太阳辐射预测。提出了一种根据站点的气象变异性选择最佳和最可靠的预测模型的方法。为了使结论更具有普遍性，使用了在低，中和高气象变异性的三个地点收集的太阳数据：阿雅克修，提洛斯和奥德洛。使用平均绝对对数返回值评估数据集的变异性。比较了模型的均方根均方误差，平均绝对误差和技能得分。为每个可变性和时间范围选择最有效的模型：对于弱可变性，自回归移动平均值和多层感知器是最有效的；对于中等变异性，自回归移动平均值和袋装回归树是最佳的对于较高的预测变量，只能有效地使用更复杂的方法，袋装回归树和随机森林方法。（C）2018 Elsevier Ltd.保留所有权利。

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《Energy》 |2018年第ptaa期|620-629|共10页
作者
Fouilloy Alexis; Voyant Cyril; Notton Gilles; Motte Fabrice; Paoli Christophe; Nivet Marie-Laure; Guillot Emmanuel; Duchaud Jean-Laurent;
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作者单位

Univ Corsica, Ctr Georges Peri, CNRS UMR SPE 6134, Route Sanguinaires, F-20000 Ajaccio, France;

Castelluccio Hosp, Radiotherapy Unit, BP 85, F-20177 Ajaccio, France;

Univ Corsica, Ctr Georges Peri, CNRS UMR SPE 6134, Route Sanguinaires, F-20000 Ajaccio, France;

Univ Corsica, Ctr Georges Peri, CNRS UMR SPE 6134, Route Sanguinaires, F-20000 Ajaccio, France;

Univ Corsica, Ctr Georges Peri, CNRS UMR SPE 6134, Route Sanguinaires, F-20000 Ajaccio, France;

Univ Corsica, Ctr Georges Peri, CNRS UMR SPE 6134, Route Sanguinaires, F-20000 Ajaccio, France;

PROMES CNRS Lab, 7 Rue 4 Solaire, F-66120 Font Romeu Odeillo Via, France;

Univ Corsica, Ctr Georges Peri, CNRS UMR SPE 6134, Route Sanguinaires, F-20000 Ajaccio, France;

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正文语种 eng
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关键词
Time series forecasting; Machine learning; Variability; ARMA; ANN; Regression tree; Gaussian process; SVR;

机译：时间序列预测;机器学习;变异性;ARMA;ANN;回归树;高斯过程;SVR;

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Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability

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