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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21720%2F24%3A00378166" target="_blank" >RIV/68407700:21720/24:00378166 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/en17133078" target="_blank" >https://doi.org/10.3390/en17133078</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/en17133078" target="_blank" >10.3390/en17133078</a>

Result language

angličtina

Original language name

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

Original language description

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 (R2), 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 R2 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Energies

ISSN

1996-1073

e-ISSN

1996-1073

Volume of the periodical

17

Issue of the periodical within the volume

13

Country of publishing house

CH - SWITZERLAND

Number of pages

23

Pages from-to

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UT code for WoS article

001266465800001

EID of the result in the Scopus database

2-s2.0-85198223451