Prediction intervals for global solar irradiation forecasting using regression trees methods

Voyant, Cyril; Motte, Fabrice; Notton, Gilles; Fouilloy, Alexis; Nivet, Marie Laure; Duchaud, Jean-Laurent

doi:10.1016/j.renene.2018.03.055

Cited by 45 publications

(34 citation statements)

References 52 publications

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“…Lastly, Voyant et al (2018) made a comparison between probabilistic forecasts using quantile regression forests and gradient boosting decision trees with past CSI observations as predictor set. They generate prediction intervals for the CSI and verify them using the gamma test, which is based on the mean interval length and prediction interval coverage probability.…”

Section: Discussionmentioning

confidence: 99%

Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation

et al. 2019

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The increased usage of solar energy places additional importance on forecasts of solar radiation. Solar panel power production is primarily driven by the amount of solar radiation and it is therefore important to have accurate forecasts of solar radiation. Accurate forecasts that also give information on the * Corresponding author ranked probability skill score, the Brier skill score and reliability diagrams. We find that quantile regression and generalized random forests generally perform best. In (near) clear sky conditions the non-parametric methods have more skill than the parametric ones.

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Section: Discussionmentioning

confidence: 99%

Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation

et al. 2019

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“…K-fold cross validation approach is a resampling procedure used to evaluate machine learning models over a sample data. The bootstrap is another resampling technique that generates multiple datasets by sampling from the original single dataset [13]. A combination of bootstrap sampling, k-fold cross validation, mutual information, and stationary time-series processing with clear sky model is applied in Ref.…”

Section: K-fold-based Methodsmentioning

confidence: 99%

“…A combination of bootstrap sampling, k-fold cross validation, mutual information, and stationary time-series processing with clear sky model is applied in Ref. [13] for solar radiation forecasting. In this method, the cumulative distribution function (CDF) and matching quintile estimation (MQE) are used to determine the prediction interval.…”

Section: K-fold-based Methodsmentioning

confidence: 99%

Pattern Recognition and Its Application in Solar Radiation Forecasting

Ghofrani¹,

Azimi²,

Youshi³

2019

Pattern Recognition - Selected Methods and Applications

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As intermittent renewable energy sources such as wind and solar proliferate, the power systems operation and planning become more complicated due to increased uncertainties and variabilities. Accurate forecasting of these sources facilitates planning and operating the electric grid to integrate wind/solar power more reliably and efficiently. The neural network learning process can be disrupted by anomalies of wind/solar time-series data, which results in less accurate forecasting. By processing and analyzing wind/solar time-series data, machine learning and pattern recognition methods such as data clustering and classification can significantly enhance the forecast accuracy. This chapter reviews the various machine learning and pattern recognition methods proposed in the literature for time-series forecasting of solar radiation.

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“…Other approaches regarding the prediction intervals of renewable resources, the price of energy, and the electricity demand have been reported ( Hu, Hu, Yue, Zhang, & Hu, 2017;Li et al, 2018;Shrivastava et al, 2015Shrivastava et al, , 2016Voyant et al, 2018 ). In the works of Shrivastava et al (2016) and Shrivastava et al (2015) , methodologies were proposed based on the support vector machine (SVM) to generate the prediction intervals for wind speed and electricity costs.…”

Section: Literature Review For Prediction Interval Modellingmentioning

confidence: 99%

“…The optimization was performed using a cost function that included the coverage probability, the sharpness of the interval and the average deviation of the data from the prediction interval as metrics. In the work of Voyant et al (2018) , prediction interval models of the global horizontal irradiation using regression tree methods were presented. Several prediction models were tested, including classic, pruned, bagged and boosted regression tree and classic and smart persistence models.…”

Section: Literature Review For Prediction Interval Modellingmentioning

confidence: 99%

Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

Marín

Cruz

Sáez

et al. 2019

Expert Systems with Applications

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Prediction interval modelling has been proposed in the literature to characterize uncertain phenomena and provide useful information from a decision-making point of view. In most of the reported studies, assumptions about the data distribution are made and/or the models are trained at one step ahead, which can decrease the quality of the interval in terms of the information about the uncertainty modelled for a higher prediction horizon. In this paper, a new prediction interval modelling methodology based on fuzzy numbers is proposed to solve the abovementioned drawbacks. Fuzzy and neural network prediction interval models are developed based on this proposed methodology by minimizing a novel criterion that includes the coverage probability and normalized average width. The fuzzy number concept is considered because the affine combination of fuzzy numbers generates, by definition, prediction intervals that can handle uncertainty without requiring assumptions about the data distribution. The developed models are compared with a covariance-based prediction interval method, and high-quality intervals are obtained, as determined by the narrower interval width of the proposed method. Additionally, the proposed prediction intervals are tested by forecasting up to two days ahead of the load of the Huatacondo microgrid in the north of Chile and the consumption of the residential dwellings in the town of Loughborough, UK. The results show that the proposed models are suitable alternatives to electrical consumption forecasting because they obtain the minimum interval widths that characterize the uncertainty of this type of stochastic process. Furthermore, the information provided by the obtained prediction interval could be used to develop robust energy management systems that, for example, consider the worst-case scenario.

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Prediction intervals for global solar irradiation forecasting using regression trees methods

Cited by 45 publications

References 52 publications

Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation

Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation

Pattern Recognition and Its Application in Solar Radiation Forecasting

Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

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