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

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.

Inspec keywords: building integrated photovoltaics; solar radiation; photovoltaic power systems; power engineering computing; weather forecasting; load forecasting; solar cells

Other keywords: decomposition models; solar spectral response; numerical weather prediction meteorological data; sufficient historical data; traditional photovoltaic forecasting method; evaluating combination models; physical prediction approach; newly built PV power plant; forecasting photovoltaic power generation; modified model; inclined surfaces; transposition models; PV array irradiance; PV power station historical power generation data; Erbs + Liu–Jordan model predictions; 12 combination forecasting models; solar irradiance; Liu–Jordan transposition model; higher forecasting accuracy

Subjects: Power engineering computing; Solar power stations and photovoltaic power systems; Solar cells and arrays

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-stg.2018.0110
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