Citation: Sansine, V.; Ortega, P.;
Hissel, D.; Hopuare, M. Solar
Irradiance Probabilistic Forecasting
Using Machine Learning,
Metaheuristic Models and Numerical
Weather Predictions. Sustainability
2022, 14, 15260. https://doi.org/
10.3390/su142215260
Academic Editors:
Luis Hernández-Callejo,
Sergio Nesmachnow and
Sara Gallardo Saavedra
Received: 9 October 2022
Accepted: 31 October 2022
Published: 17 November 2022
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Article
Solar Irradiance Probabilistic Forecasting Using Machine
Learning, Metaheuristic Models and Numerical
Weather Predictions
Vateanui Sansine
1,2,
*, Pascal Ortega
1
, Daniel Hissel
2
and Marania Hopuare
1
1
GEPASUD, Université de Polynésie Française, Campus d’Outumaoro,
98718 Puna’auia, Tahiti, French Polynesia
2
FEMTO-ST/FCLAB, Université de Franche-Comté, CNRS, Rue Thierry Meg, CEDEX, F-90010 Belfort, France
* Correspondence: vateanui.sansine@doctorant.upf.pf; Tel.: +689-40-80-38-76
Abstract:
Solar-power-generation forecasting tools are essential for microgrid stability, operation, and
planning. The prediction of solar irradiance (SI) usually relies on the time series of SI and other mete-
orological data. In this study, the considered microgrid was a combined cold- and power-generation
system, located in Tahiti. Point forecasts were obtained using a particle swarm optimization (PSO)
algorithm combined with three stand-alone models: XGboost (PSO-XGboost), the long short-term
memory neural network (PSO-LSTM), and the gradient boosting regression algorithm (PSO-GBRT).
The implemented daily SI forecasts relied on an hourly time-step. The input data were composed
of outputs from the numerical forecasting model AROME (Météo France) combined with historical
meteorological data. Our three hybrid models were compared with other stand-alone models, namely,
artificial neural network (ANN), convolutional neural network (CNN), random forest (RF), LSTM,
GBRT, and XGboost. The probabilistic forecasts were obtained by mapping the quantiles of the
hourly residuals, which enabled the computation of 38%, 68%, 95%, and 99% prediction intervals
(PIs). The experimental results showed that PSO-LSTM had the best accuracy for day-ahead solar
irradiance forecasting compared with the other benchmark models, through overall deterministic
and probabilistic metrics.
Keywords:
solar irradiance; forecasting; numerical weather predictions; machine learning; deep
learning; metaheuristic models; optimization
1. Introduction
Global electricity demand is expected to rise by 2.4% in 2022, despite economic weak-
nesses and high prices [
1
]. This rise, driven by the growth of the world population, the
industrialization of developing countries, and the worldwide process of urbanization [
2
],
uses fossil fuels as the main power source. This has proven to be detrimental for the
environment and the climate. Therefore, renewable energies have gained a lot of attention,
especially photovoltaics (PVs), due to their accessibility, low cost, lifetime, and environ-
mental benefits. Solar PV installations are growing faster than any other renewable energy.
Indeed, PVs are forecast to account for 60% of the increase in global renewable capacity in
2022 [
3
]. In this context, PVs provide many environmental and economic benefits. However,
uncontrollable factors such as the weather, seasonality, and climate lead to intermittent,
random, and volatile PV power generation. These significant constraints still hinder the
large-scale integration of PVs into the power grid and interfere with the reliability and
stability of existing grid-connected power systems [
4
]. Thus, a reliable forecast of PV
power outputs is essential to ensure the stability, reliability, and cost-effectiveness of the
system [
5
]. Those forecasts are usually implemented through prediction of the global
horizontal irradiance (GHI). There are three main groups of solar irradiance forecasting
model [6]:
Sustainability 2022, 14, 15260. https://doi.org/10.3390/su142215260 https://www.mdpi.com/journal/sustainability
