A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models
The uncertainty associated with modeling and performance prediction of solar photovoltaic
systems could be easily and efficiently solved by artificial intelligence techniques. During the …
systems could be easily and efficiently solved by artificial intelligence techniques. During the …
Evaluation and development of empirical models for estimating daily and monthly mean daily diffuse horizontal solar radiation for different climatic regions of China
The diffuse solar radiation is required for estimating global solar radiation on inclined
surface or for estimating beam solar radiation for concentrating photovoltaic applications. In …
surface or for estimating beam solar radiation for concentrating photovoltaic applications. In …
Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions
Increasing air pollutants significantly affect the proportion of diffuse (R d) to global (R s) solar
radiation. This study proposed three new hybrid support vector machines (SVM) with particle …
radiation. This study proposed three new hybrid support vector machines (SVM) with particle …
A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions
In this study, the potential of six different machine learning models, gradient boosting tree
(GBT), multilayer perceptron neural network (MLPNN), two types of adaptive neuro-fuzzy …
(GBT), multilayer perceptron neural network (MLPNN), two types of adaptive neuro-fuzzy …
Evaluating the most significant input parameters for forecasting global solar radiation of different sequences based on Informer
C Jiang, Q Zhu - Applied Energy, 2023 - Elsevier
The number of existing global solar radiation (GSR) observation stations is limited, and it is
challenging to meet the demand for scientific research and production. Different forecasting …
challenging to meet the demand for scientific research and production. Different forecasting …
Solar radiation prediction using different machine learning algorithms and implications for extreme climate events
L Huang, J Kang, M Wan, L Fang, C Zhang… - Frontiers in Earth …, 2021 - frontiersin.org
Solar radiation is the Earth's primary source of energy and has an important role in the
surface radiation balance, hydrological cycles, vegetation photosynthesis, and weather and …
surface radiation balance, hydrological cycles, vegetation photosynthesis, and weather and …
Feature selection in machine learning prediction systems for renewable energy applications
This paper focuses on feature selection problems that arise in renewable energy
applications. Feature selection is an important problem in machine learning, both in …
applications. Feature selection is an important problem in machine learning, both in …
Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree
In this study, four different heuristic regression methods including Kriging, response surface
method (RSM), multivariate adaptive regression (MARS) and M5 model tree (M5Tree) have …
method (RSM), multivariate adaptive regression (MARS) and M5 model tree (M5Tree) have …
Predicting daily diffuse horizontal solar radiation in various climatic regions of China using support vector machine and tree-based soft computing models with local …
Abstract Knowledge of diffuse horizontal solar radiation (R d) on horizontal surfaces is a
prerequisite for the design and optimization of active and passive solar energy systems such …
prerequisite for the design and optimization of active and passive solar energy systems such …
A novel combined multi-task learning and Gaussian process regression model for the prediction of multi-timescale and multi-component of solar radiation
A novel combined multi-task learning and Gaussian process regression (MTGPR) model is
proposed to predict the multi-time scale (daily and monthly mean daily) and multi …
proposed to predict the multi-time scale (daily and monthly mean daily) and multi …