Statistical analysis of multi‐day solar irradiance using a threshold time series model
Statistical analysis of multi‐day solar irradiance using a threshold time series model
复制标题
使用阈值时间序列模型对多日太阳辐照度进行统计分析
DOI：
10.1002/env.2716
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发表时间：
2022
期刊：
影响因子：
1.7
通讯作者：
B. Reich
中科院分区：
文献类型：
--
作者：
C. Euán;Ying Sun;B. Reich
太阳辐照度的分析在预测太阳能发电厂的太阳能产量方面具有重要的应用。虽然太阳每天提供的能量超过我们的需求，但环境条件引起的变化会影响电力生产。最近，已经提出了新的统计模型，以提供高分辨率数据的随机模拟，以缩小和预测太阳辐照度测量。大多数现有的模型是线性的，高度依赖于正态假设。然而，太阳辐照度显示出很强的非线性，并且只能在白天测量。因此，我们提出了一个新的多日阈值自回归模型来量化日辐照度时间序列的变异性。我们建立了我们的模型是平稳的充分条件，我们开发了一个推理过程来估计模型参数。当我们应用我们的模型来研究观测到的辐照度数据的统计特性在瓜德罗普岛群，法国海外地区位于南加勒比海，我们能够表征两个国家的辐照度系列。这些状态代表晴空和非晴空制度。使用我们的模型，我们能够模拟辐照度系列，表现类似于真实的数据的平均值和变异性，更准确的预测相比，线性模型。
The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Recently, new statistical models have been proposed to provide stochastic simulations of high‐resolution data to downscale and forecast solar irradiance measurements. Most of the existing models are linear and highly depend on normality assumptions. However, solar irradiance shows strong nonlinearity and is only measured during the day time. Thus, we propose a new multi‐day threshold autoregressive model to quantify the variability of the daily irradiance time series. We establish the sufficient conditions for our model to be stationary, and we develop an inferential procedure to estimate the model parameters. When we apply our model to study the statistical properties of observed irradiance data in Guadeloupe island group, a French overseas region located in the Southern Caribbean Sea, we are able to characterize two states of the irradiance series. These states represent the clear‐sky and non‐clear sky regimes. Using our model, we are able to simulate irradiance series that behave similarly to the real data in mean and variability, and more accurate forecasts compared to linear models.
影响因子：
1.7
作者：
Wenqi Zhang;W. Kleiber;B. Hodge;B. Mather
通讯作者：
Wenqi Zhang;W. Kleiber;B. Hodge;B. Mather
影响因子：
3
作者：
Zhang, Wenqi;Kleiber, William;Florita, Anthony R.;Hodge, Bri-Mathias;Mather, Barry
通讯作者：
Mather, Barry
DOI：
10.1214/21-aoas1455
发表时间：
2021-09
期刊：
The annals of applied statistics
影响因子：
--
作者：
Hadj-Amar B;Finkenstädt B;Fiecas M;Huckstepp R
通讯作者：
Huckstepp R