Evaluating combination models of solar irradiance on inclined surfaces and forecasting photovoltaic power generation

Abstract

The traditional photovoltaic (PV) forecasting method depends on sufficient historical data (PV power station historical power generation data and numerical weather prediction meteorological data), which is not suitable for a newly built PV power plant. In order to calculate the PV array irradiance and to predict the PV power, a physical prediction approach based on solar irradiance on inclined surfaces is proposed. This method selects three decomposition models and four transposition models to be combined into 12 combination forecasting models. Furthermore, solar spectral response, incidence angle, and soiling factor are taken into account in the modified model. The results show that the methods combining the Liu–Jordan transposition model have higher forecasting accuracy under the different weather types. Among them, the Erbs + Liu–Jordan model predictions are the most accurate.

Keywords

• solar radiation
• load forecasting
• solar cells
• power engineering computing
• photovoltaic power systems
• building integrated photovoltaics
• weather forecasting
• inclined surfaces
• forecasting photovoltaic power generation
• traditional photovoltaic forecasting method
• sufficient historical data
• PV power station historical power generation data
• numerical weather prediction meteorological data
• newly built PV power plant
• PV array irradiance
• physical prediction approach
• solar irradiance
• decomposition models
• transposition models
• 12 combination forecasting models
• solar spectral response
• modified model
• Liu–Jordan transposition model
• higher forecasting accuracy
• Erbs + Liu–Jordan model predictions
• evaluating combination models