Article
Enhanced Random Forest Model for Robust Short-Term
Photovoltaic Power Forecasting Using Weather Measurements
Mohamed Massaoudi
1,2,
* , Ines Chihi
3,4,5
, Lilia Sidhom
4,5
, Mohamed Trabelsi
6
, Shady S. Refaat
1
and Fakhreddine S. Oueslati
2
Citation: Massaoudi, M.; Chihi, I.;
Sidhom, L.; Trabelsi, M.; Refaat, S.S.;
Oueslati, F.S. Enhanced Random
Forest Model for Robust Short-Term
Photovoltaic Power Forecasting
Using Weather Measurements.
Energies 2021, 14, 3992. https://
doi.org/10.3390/en14133992
Academic Editor: Alon Kuperman
Received: 27 May 2021
Accepted: 28 June 2021
Published: 2 July 2021
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1
Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha 3263, Qatar;
Shady.khalil@qatar.tamu.edu
2
Laboratoire Matériaux Molécules et Applications (LMMA) à l’IPEST, Carthage University, Tunis 1054, Tunisia;
fakhreddine.oueslati@enicarthage.rnu.tn
3
Département Ingénierie, Faculté des Sciences, des Technologies et de Médecine, Campus Kirchberg,
Université du Luxembourg, 1359 Luxembourg, Luxembourg; ines.chihi@uni.lu
4
Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), El Manar University,
Tunis 1068, Tunisia; Lilia.Sidhom@enib.rnu.tn
5
National Engineering School of Bizerta, Carthage University, Tunis 7080, Tunisia
6
Department of Electronic and Communications Engineering, Kuwait College of Science and Technology,
Doha District, Block 4, Doha P.O. Box 27235, Kuwait; m.trabelsi@kcst.edu.kw
* Correspondence: mohamed.massaoudi@qatar.tamu.edu; Tel.: +974-70-062-097
Abstract:
Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost
importance in smart grids. The deployment of STPF techniques provides fast dispatching in the
case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-
driven method based on enhanced Random Forest (RF) model. The proposed method employs an
ensemble of attribute selection techniques to manage bias/variance optimization for STPF application
and enhance the forecasting quality results. The overall architecture strategy gathers the relevant
information to constitute a voted feature-weighting vector of weather inputs. The main emphasis
in this paper is laid on the knowledge expertise obtained from weather measurements. The feature
selection techniques are based on local Interpretable Model-Agnostic Explanations, Extreme Boosting
Model, and Elastic Net. A comparative performance investigation using an actual database, collected
from the weather sensors, demonstrates the superiority of the proposed technique versus several
data-driven machine learning models when applied to a typical distributed PV system.
Keywords:
smart grid; Photovoltaic (PV) Power Forecasting; weather sensors; random decision
forest; feature importance; energy management
1. Introduction
Over the years, the exponential increase in global energy demand has become the
leading cause of the rapid depletion of fossil fuels and increased Greenhouse Gas (GHG)
emissions of conventional generators [
1
]. To effectively satisfy the meteoric growth in
energy consumption, the world has taken serious initiatives to deploy RES on a larger
scale. [2].
Solar Energy (SE) hold out the greatest promise for modern humankind among all
RES, being free, clean, and abundantly available [
3
]. For these reasons, it keeps increasing
its share in the energy-mix in the face of diminishing conventional fossil fuel energy sources
and rising environmental protection concerns [
3
]. However, the discontinuity of PV power
flow brings into question the reliability of the high penetration of PV systems, which affect
the dispatch accuracy greatly. Moreover, the negative effects of the sudden weather change
on the PV farms threatens the grid stability and rises the cumbersome costs of the allocation
of the spinning reserve [
3
]. Therefore, PV Power Forecasting (PPF) is a pivotal element
for reliable power supply as it significantly reduces the sensitivity of energy systems to
weather intermittency. PPF is mandatory for PV generators as it has a direct impact on the
Energies 2021, 14, 3992. https://doi.org/10.3390/en14133992 https://www.mdpi.com/journal/energies
