Comparing Beta Regression and Quantile Regression Forests for Probabilistic Photovoltaic Energy Forecasting⁎

Q3 Engineering

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI:10.1016/j.ifacol.2025.03.056

Marco Capelletti , Valentina Fogli , Edoardo Santilli , Matteo Gardini , Riccardo Vignali , Giuseppe De Nicolao

引用次数: 0

Abstract

This work focuses on the analysis of probabilistic power curve models that link the energy production of a photovoltaic system with the irradiance forecasts of the weather provider. It is shown that the essential information is provided by global horizontal irradiance (GHI) forecasts, which can be used to obtain simple and meaningful probabilistic models using non-parametric (quantile Regression forests) and parametric (Beta regression) methods. The second step is the analysis of the monthly performance of power curve models, to understand whether their performance can improve by taking monthly variability into account.

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比较Beta回归和分位数回归森林在概率光伏能源预测中的应用

这项工作的重点是分析概率功率曲线模型，该模型将光伏系统的能源生产与天气提供商的辐照度预测联系起来。结果表明，全球水平辐射（GHI）预报提供了基本信息，可以利用非参数（分位数回归森林）和参数（Beta回归）方法获得简单而有意义的概率模型。第二步是对功率曲线模型的月度表现进行分析，通过考虑月度变异性来了解其性能是否可以得到改善。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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