Abstract
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.
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Taomae, T., Lim, L., Stevens, D., Nakafuji, D. (2018). 30 min-Ahead Gridded Solar Irradiance Forecasting Using Satellite Data. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds) Databases Theory and Applications. ADC 2018. Lecture Notes in Computer Science(), vol 10837. Springer, Cham. https://doi.org/10.1007/978-3-319-92013-9_15
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DOI: https://doi.org/10.1007/978-3-319-92013-9_15
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