Improving Photovoltaic Power Prediction: Insights through Computational Modeling and Feature Selection

Ahmed Faris Amiri, Aissa Chouder, Houcine Oudira, Santiago Silvestre () and Sofiane Kichou
Additional contact information
Ahmed Faris Amiri: Laboratory of Electrical Engineering (LGE), Electronic Department, University of M’sila, P.O. Box 166 Ichebilia, M’sila 28000, Algeria
Aissa Chouder: Laboratory of Electrical Engineering (LGE), Electronic Department, University of M’sila, P.O. Box 166 Ichebilia, M’sila 28000, Algeria
Houcine Oudira: Laboratory of Electrical Engineering (LGE), Electronic Department, University of M’sila, P.O. Box 166 Ichebilia, M’sila 28000, Algeria
Santiago Silvestre: Department of Electronic Engineering, Universitat Politècnica de Catalunya (UPC), Mòdul C5 Campus Nord UPC, Jordi Girona 1-3, 08034 Barcelona, Spain
Sofiane Kichou: University Centre for Energy Efficient Buildings, Czech Technical University in Prague, 1024 Třinecká St., 27343 Buštěhrad, Czech Republic

Energies, 2024, vol. 17, issue 13, 1-23

Abstract: This work identifies the most effective machine learning techniques and supervised learning models to estimate power output from photovoltaic (PV) plants precisely. The performance of various regression models is analyzed by harnessing experimental data, including Random Forest regressor, Support Vector regression (SVR), Multi-layer Perceptron regressor (MLP), Linear regressor (LR), Gradient Boosting, k-Nearest Neighbors regressor (KNN), Ridge regressor (Rr), Lasso regressor (Lsr), Polynomial regressor (Plr) and XGBoost regressor (XGB). The methodology applied starts with meticulous data preprocessing steps to ensure dataset integrity. Following the preprocessing phase, which entails eliminating missing values and outliers using Isolation Feature selection based on a correlation threshold is performed to identify relevant parameters for accurate prediction in PV systems. Subsequently, Isolation Forest is employed for outlier detection, followed by model training and evaluation using key performance metrics such as Root-Mean-Squared Error (RMSE), Normalized Root-Mean-Squared Error (NRMSE), Mean Absolute Error (MAE), and R-squared (R 2 ), Integral Absolute Error (IAE), and Standard Deviation of the Difference (SDD). Among the models evaluated, Random Forest emerges as the top performer, highlighting promising results with an RMSE of 19.413, NRMSE of 0.048%, and an R 2 score of 0.968. Furthermore, the Random Forest regressor (the best-performing model) is integrated into a MATLAB application for real-time predictions, enhancing its usability and accessibility for a wide range of applications in renewable energy.

Keywords: PV prediction; computational modeling; regression techniques (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/13/3078/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/13/3078/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:13:p:3078-:d:1419898

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().