Abstract
The tropical insular region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting. Therefore, it is necessary to develop and use performant and robustness forecasting techniques. This paper examines the predictive performance of a novel solar forecasting approach, the multiscale hybrid forecast model (MHFM), as a function of several parameters. The MHFM model is a technique recently used for irradiance forecasting based on a hybrid autoregressive (AR) and neural network (NN) model combined with multiscale decomposition methods. This technique presents a relevant performance for 1 h ahead global horizontal irradiance forecast. The goal of this work is to highlight the strength and limits of this model by assessing the influence of different parameters from a metric error analysis. This study illustrates modeling process performance as a function of daily insolation conditions and testifies the influence of learning data and test data time scales. Several forecast horizon strategies and their influence on the MHFM performance were investigated. With the best strategy, a rRMSE value from 4.43 % to 10.24 % was obtained for forecast horizons from 5 min to 6 h. The analysis of intra-day solar resource variability showed that the best performance of MHFM was obtained for clear sky days with a rRMSE of 2.91 % and worst for cloudy sky days with a rRMSE of 6.73 % . These works constitute an additional analysis in agreement with the literature about influence of daily insolation conditions and horizons time scales on modeling process.
Highlights
Solar forecasts are essential for grid-connected solar photovoltaics (PV) as penetration increases
Firstly the results of our analysis on the multiscale hybrid forecast model (MHFM) predictive performance are presented as a function of the two forecast horizon strategies previously described in this paper (Section 2.4.1)
The main cause of variability in surface solar irradiance is the motion/evolution of clouds. This is a acute issue when considering grid-connected PV development for small island grids that are not interconnected without a possibility of spatial smoothing
Summary
Solar forecasts are essential for grid-connected solar photovoltaics (PV) as penetration increases. The frequent cloud formation with a diversity of solar microclimates leads to a challenging solar forecasting. Better solar forecasting tools contribute to improving the integration of this energy in the electric network. There is a rich literature on forecasting techniques (see [1,2,3] for a comprehensive review): methods using mathematical formalism of times series, numerical weather prediction (NWP) model and weather satellite imagery. Methods using mathematical formalism of time series show relevant predictive performance for horizons lower than one day (short time scales: from few minutes to few hours) such as connectionist models (artificial neural network) and more the Multi-Layer Perceptron (MLP) , which is the most often
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