Machine learning methods for solar radiation forecasting: A review

Abstract

Forecasting the output power of solar systems is required for the good operation of the power grid or for the optimal management of the energy fluxes occurring into the solar system. Before forecasting the solar systems output, it is essential to focus the prediction on the solar irradiance. The global solar radiation forecasting can be performed by several methods; the two big categories are the cloud imagery combined with physical models, and the machine learning models. In this context, the objective of this paper is to give an overview of forecasting methods of solar irradiation using machine learning approaches. Although, a lot of papers describes methodologies like neural networks or support vector regression, it will be shown that other methods (regression tree, random forest, gradient boosting and many others) begin to be used in this context of prediction. The performance ranking of such methods is complicated due to the diversity of the data set, time step, forecasting horizon, set up and performance indicators. Overall, the error of prediction is quite equivalent. To improve the prediction performance some authors proposed the use of hybrid models or to use an ensemble forecast approach.

A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

“…As shown in subsection 3.3 the utilization of optical flow estimation for a short-term forecast of the effective cloud albedo and hence of the solar surface irradiance shows promising results. Validation results reported in recent review publications by Voyant et al [9], Antonanzas et al [10] or Barbieri et al [11] or rather publications by other leading experts, for example Raza et al [12], Wolff et al [1] or Cros et al [34] do not provide any hints that the Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 April 2018 doi:10.20944/preprints201804.0367.v1 application of the widely used neuronal networks lead to a significant better accuracy for cloud motion vectors. For example in Cros et al [34] the RMSE of the 30-minute forecast of the effective cloud albedo is about 30 % for a neuronal network state of the art approach and a phase correlation method.…”

“…Once a successful training has been performed, the execution of the code is very fast. Voyant et al [9] provides a review and overview of neuronal network methods applied within the forecast of solar surface radiation. A disadvantage of neuronal networks is their black box character.…”

Predicting clear-sky global horizontal irradiance at eight locations in South Africa using four models

“…The ANNs approximate the functional relationship between random input and output variables by learning from examples made up of historical data output and input variables [17]. Published applications of ANNs in the field of solar energy include time-series forecasting of solar radiation quantities [18][19][20][21] and other function approximation or regression models that map a set of input parameters like temperature into radiation quantities [22][23][24][25][26]. One major attraction of ANN methods is their ability to find relations between input and output even if the representation was intractable [19].…”

Data-Mining-based filtering to support Solar Forecasting Methodologies

“…Usually to predict renewable sources of energy two approaches may be used: an approach based on physical models (Badescu, 2008), using mathematical equations to describe physics and dynamics of the atmosphere that influences solar radiation, and an approach based on time series analysis by means of statistical models (Paolik (Inman et al, 2013), (Pelland, 2013), (Diagne et al, 2013), (Mohanty et al, 2017) provide good overviews on the current state of the art in solar irradiance forecasting, while (Voyant, 2017) provides a more specific review on the application of machine learning methods for solar radiation forecasting. The work proposed in (Barzin, 2016) suggests that the use of gradient boosted regression trees can be a valid solution for multi-site solar power forecasting.…”