Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Machine Learning Approach for Solar Irradiance Estimation on Tilted Surfaces in Comparison with Sky Models Prediction

Domaine:

environment and energy
Créateur:
O. C. R. C.
Éditeur:
Sum
Hôte:
In this study, two supervised machine learning models (Extreme Gradient Boosting and K-nearest Neighbour) and four isotropic sky models (Liu and Jordan, Badescu, Koronakis, and Tian) were employed to estimate global solar radiation on daily data measured for one year period at the National Center for Energy, Research and Development (NCERD) at the University of Nigeria, Nsukka. Two solarimeters were employed to measure solar radiation: one measured solar radiation on a tilted surface at a 15° angle of tilt, facing south, and the other measured global horizontal solar radiation. The measured global horizontal solar radiation and the time and day number were used as input for the prediction process. Python computational software was used for model prediction, and the performance of each model was assessed using statistical methods such as mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE) (RMSE). Compared to the measured data, it was discovered that the Extreme Gradient Boosting (XGBoost) algorithm offered the best performance with the least inaccuracy to sky models.

Visit

doi.org

Languages

Igbo

Similaires

Machine Learning Approach for Short- and Long-Term Global Solar Irradiance PredictionExtreme Gradient Boosting: A Machine Learning Technique for Daily Global Solar Radiation Forecasting on Tilted SurfacesSolar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparisonAdvancing Very Short-Term Solar Irradiance Forecasting in Africa: a Low-Cost Sky Imaging and Machine Learning-Based ApproachSemi-empirical models for the estimation of global solar irradiance measurements in MoroccoRainfall Prediction Models for Katsina State, Nigeria: Machine Learning Approach

Machine Learning Approach for Short- and Long-Term Global Solar Irradiance Prediction

Solar radiation data forecasting algorithms are important, especially in developing countries, as va

Extreme Gradient Boosting: A Machine Learning Technique for Daily Global Solar Radiation Forecasting on Tilted Surfaces

Enhancing solar irradiance and accurate forecasting is required for improved performance of photovol

Solar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparison

Advancing Very Short-Term Solar Irradiance Forecasting in Africa: a Low-Cost Sky Imaging and Machine Learning-Based Approach

Africa holds immense potential for solar energy, thanks to its high year-round solar irradiation. Ad

Semi-empirical models for the estimation of global solar irradiance measurements in Morocco

International audience This paper presents semi-empirical models for estimating Globa

Rainfall Prediction Models for Katsina State, Nigeria: Machine Learning Approach

Weather patterns and rainfall are essential pieces of information that drive the agricultural sector