30 min-Ahead Gridded Solar Irradiance Forecasting Using Satellite Data

被引:0
作者
Taomae, Todd [1 ]
Lim, Lipyeow [1 ]
Stevens, Duane [1 ]
Nakafuji, Dora [2 ]
机构
[1] Univ Hawaii Manoa, Honolulu, HI 96822 USA
[2] Hawaiian Elect Co, Honolulu, HI USA
来源
DATABASES THEORY AND APPLICATIONS, ADC 2018 | 2018年 / 10837卷
关键词
NEURAL-NETWORKS; RADIATION; MODEL;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Solar irradiance forecasting is critical to balancing solar energy production and energy consumption in the electric grid; however, solar irradiance forecasting is dependent on meteorological conditions and, in particular, cloud cover, which are captured in satellite imagery. In this paper we present a method for short-term solar irradiance forecasting using gridded global horizontal irradiance (GHI) data estimated from satellite images. We use this data to first create a simple linear regression model with a single predictor variable. We then discuss various methods to extend and improve the model. We found that adding predictor variables and partitioning the data to create multiple models both reduced prediction errors under certain circumstances. However, both these techniques were outperformed by applying a data transformation before training the linear regression model.
引用
收藏
页码:186 / 198
页数:13
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