Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review

Abstract

The use of intelligent algorithms for global solar prediction is an ideal tool for research focused on the use of solar energy. Forecasting solar radiation supports different applications focused on the generation and transport of energy in places where there are no meteorological stations. Different solar radiation prediction techniques have been applied in different time horizons, such as neural networks (ANN) or machine learning (ML), with the latter being the most important. The support vector machine (SVM) is a classification method of the ML that is used to predict solar radiation. To obtain a better accuracy of prediction data, search optimization algorithms (SOA) such as genetic algorithms (GA) and the particle swarm optimization algorithm (PSO) were used to optimize the prediction accuracy by searching the model parameters. This article presents a review of different hybrid SVM models with SOA applied to obtain the best parameters to reduce the prediction error of solar radiation using meteorological variables. Research articles from the last 5 years on solar radiation prediction using SVM models and hybrid SMV optimized models with SOA were studied. The results show that SVM with GA presents a better performance than the classical SVM models using the Radial basis kernel function for prediction parameters.

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

“…Although there are equations and formulas already established to calculate the network error, which is a sought-after parameter in control theory and ANN, only some articles mention this point [ 58 ]. Equations such as Mean absolute percentage error (MAPE), Root mean square error (RMSE), Mean bias error (MBE), and Mean absolute error (MAE) help us to know the performance of our algorithm [ 23 ].…”

“…In recent years, metaheuristic algorithms have been considered, primarily designed to solve complex problems with multiple variables and obtain the most optimal possible values [ 23 ]. Algorithms such as Ant Colony (ACO) [ 24 , 25 ], Cuckoo Search (CS) [ 26 , 27 ], Firefly Algorithm (FF) [ 28 , 29 ], Whale Optimization (WO) [ 30 , 31 ], Differential Evolution (DE) [ 32 ], Gray Wolf (GWO) [ 33 , 34 ] and Particle Swarm (PSO) [ 35 , 36 ].…”

Efficiency and Optimization of Buildings Energy Consumption Volume II

“…In consequence, the key is to define a correct algorithm based on correct variables (such as weather conditions [6]) and, at other times, to employ a more adequate machine learning method. In this sense, a Support Vector Machine [7] was shown to be adequate for global solar prediction, but short memory neural networks were preferred for the prediction of other classical variables related to energy consumption in buildings, such as thermal inertia and input time lag [8]. More examples of machine learning methods were employed to predict [9] and to define building parameters like the Heat Loss Coefficient [8], reaching a maximum error of 6%.…”

Multicolor Fluorescence Imaging for the Early Detection of Salt Stress in Arabidopsis

“…After the application of PCA, the principal components were uncorrelated variables that successively maximized variance and then the ideal principal component features were determined. An SVM is a versatile and configurable model that could be treated as a classification problem, which has a better performance than other traditional machine learning algorithms [31,32]. Therefore, it is a great alternative to classify the salt-stressed plants from the controls based on the model that combines PCA and SVM.…”