Abstract
Solar power poses challenges to the management of grid energy due to its intermittency. To have an optimal integration of solar power on the electricity grid it is important to have accurate forecasts. This study discusses the comparative analysis of semi-parametric extremal mixture (SPEM), generalised additive extreme value (GAEV) or quantile regression via asymmetric Laplace distribution (QR-ALD), additive quantile regression (AQR-1), additive quantile regression with temperature variable (AQR-2) and penalised cubic regression smoothing spline (benchmark) models for probabilistic forecasting of hourly global horizontal irradiance (GHI) at extremely high quantiles (<img src=image/13425194_01.gif> = 0.95, 0.97, 0.99, 0.999 and 0.9999). The data used are from the University of Venda radiometric in South Africa and are from the period 1 January 2020 to 31 December 2020. Empirical results from the study showed that the AQR-2 is the best fitting model and gives the most accurate prediction of quantiles at <img src=image/13425194_01.gif> = 0.95, 0.97, 0.99 and 0.999, while at 0.9999-quantile the GAEV model has the most accurate predictions. Based on these results it is recommended that the AQR-2 and GAEV models be used for predicting extremely high quantiles of hourly GHI in South Africa. The predictions from this study are valuable to power utility decision-makers and system operators when making high-risk decisions and regulatory frameworks that require high-security levels. This is the first application to conduct a comparative analysis of the proposed models using South African solar irradiance data, to the best of our knowledge.
Highlights
1.1 BackgroundThe generation of power from clean energy sources makes an important contribution to sustainable development
This study discusses the comparative analysis of semi-parametric extremal mixture (SPEM), generalised additive extreme value (GAEV) or quantile regression via asymmetric Laplace distribution (QR-ALD), additive quantile regression (AQR-1), additive quantile regression with temperature variable (AQR-2) and penalised cubic regression smoothing spline models for probabilistic forecasting of hourly global horizontal irradiance (GHI) at extremely high quantiles (τ = 0.95, 0.97, 0.99, 0.999 and 0.9999)
The main contribution of this study is that it compares semi-parametric extremal mixture (SPEM), generalised additive extreme value (GAEV) or quantile regression via asymmetric Laplace distribution (QR-ALD), additive quantile regression (AQR-1), additive quantile regression with temperature variable (AQR-2) and penalised cubic regression smoothing spline models in predicting extremely high hourly GHI data
Summary
The generation of power from clean energy sources makes an important contribution to sustainable development. Accurate solar power forecasts are useful for an economic operation dispatch, optimal unit commitment and ensuring the stability of a national grid It reduces the uncertainties of solar energy sources and results in assuring safety and easier grid management. It has been shown that solar power forecasting is essential in different areas of planning operations in the energy sector such as unit dispatch and renders schedules of production of power from renewable energy sources for the hours or days. These forecasts are used to know in advance the amount of solar power that will be integrated into the grid in the following hours or days. The purpose of producing new forecasting models for solar power is to provide accurate forecasts [6]
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