Publications by authors named "Amon Masache"

The renewable energy industry requires accurate forecasts of intermittent solar irradiance (SI) to effectively manage solar power generation and supply. Introducing the random forests (RFs) model and its hybridisation with quantile regression modelling, the quantile regression random forest (QRRF), can help improve the forecasts' accuracy. This paper assesses the RFs and QRRF models against the quantile generalised additive model (QGAM) by evaluating their forecast performances.

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Article Synopsis
  • The study focuses on improving solar irradiation forecasting by modeling additive effects, employing non-parametric quantile regression (QR) to enhance prediction accuracy.
  • It compares three models: partial linearly additive quantile regression (PLAQR), additive quantile regression (AQR), and quantile generalized additive model (QGAM) using various forecasting metrics, with QGAM showing slightly better performance overall.
  • Results indicate that while model performance varies by location, forecasting horizon and sample size significantly affect the effectiveness of each model, suggesting that the choice of model may depend on specific forecasting requirements.
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