Global solar irradiation prediction using a multi-gene genetic programming approach

  title={Global solar irradiation prediction using a multi-gene genetic programming approach},
  author={Indranil Pan and Daya Shankar Pandey and Saptarshi Das},
In this paper, a nonlinear symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for a data-driven modelling between the dependent and the independent variables. The technique is applied for modelling the measured global solar irradiation and validated through numerical simulations. The proposed modelling technique shows improved results over the fuzzy logic and artificial neural network (ANN) based approaches as attempted by… 
Estimating solar irradiance using genetic programming technique and meteorological records
Solar irradiance is one of the most important parameters that need to be estimated and modeled before engaging in any solar energy project. This article describes a non-linear regression model based
A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study
A new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data is proposed and it is revealed that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS -SVM and the stand-alone LS-S VM.
Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks
Evaluating two machine learning algorithms for intraday solar irradiance forecasting, multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN), found that MGGP presented more accurate and robust results in single prediction cases, providing faster solutions, while ANN present more accurate results for ensemble forecasting, although it presented higher complexity and requires additional computational effort.
A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
GPSFARPS, a software application to provide a robust solution to the prediction of SFR using an evolutionary algorithm known as multi-gene genetic programming, which shows its unique ability to evolve a suitable failure rate expression with a fast convergence at 30 generations from a maximum specified generation of 500.
Development of a genetic algorithm for maximizing wind power integration rate into the electric grid
A new method was proposed with the objective of maximizing the rate of wind power integration into the electric grid based on the optimization of the parameters of the turbine governors (TGs) by means of a genetic algorithm.
Investigating the impact of input variable selection on daily solar radiation prediction accuracy using data-driven models: a case study in northern Iran
Data-driven models have been explored in numerous studies for solar radiation ( $${R}_{s}$$ R s ) prediction. However, the use of different input variable selection (IVS) methods for improving


A linear genetic programming approach for the prediction of solar global radiation
The results indicate that the LGP models give precise estimations of the solar global radiation and significantly outperform traditional angstrom’s model.
Modeling global solar radiation using Particle Swarm Optimization (PSO)
Solar resource estimation using artificial neural networks and comparison with other correlation models
Artificial Neural Network (ANN) based models for estimation of monthly mean daily and hourly values of solar global radiation are presented in this paper. Solar radiation data from 13 stations spread
ANN-based modelling and estimation of daily global solar radiation data: A case study
In this paper, an artificial neural network (ANN) models for estimating and modelling of daily global solar radiation have been developed. The data used in this work are the global irradiation HG,
A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems
This paper presents a new approach for behavioral modeling of structural engineering systems using a promising variant of genetic programming (GP), namely multi-gene genetic programming (MGGP). MGGP
Prediction of monthly mean daily global solar radiation using Artificial Neural Network
In this study, a multilayer feed forward (MLFF) neural network based on back propagation algorithm was developed, trained, and tested to predict monthly mean daily global radiation in Tamil Nadu,
Artificial Neural Network based Prediction of Solar Radiation for Indian Stations
An ANN model is developed which can be used to predict solar radiation at any given location in India and it is found that RMSE in the ANN model varies 0.0486–3.562 for Indian region.