Journal ArticleDOI
A support vector machine–firefly algorithm-based model for global solar radiation prediction
Lanre Olatomiwa,Lanre Olatomiwa,Saad Mekhilef,Shahaboddin Shamshirband,Kasra Mohammadi,Dalibor Petković,Ch. Sudheer +6 more
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TLDR
In this article, a hybrid machine learning technique for solar radiation prediction based on some meteorological data is examined, which is developed by hybridizing the Support Vector Machines (SVMs) with Firefly Algorithm (FFA) to predict the monthly mean horizontal global solar radiation using three meteorological parameters of sunshine duration (n¯), maximum temperature (Tmax), and minimum temperature(Tmin) as inputs.About:
This article is published in Solar Energy.The article was published on 2015-05-01. It has received 289 citations till now. The article focuses on the topics: Mean absolute percentage error.read more
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Solar photovoltaic generation forecasting methods: A review
TL;DR: In this article, an extensive review on recent advancements in the field of solar photovoltaic power forecasting is presented, which aims to analyze and compare various methods of solar PV power forecasting in terms of characteristics and performance.
Journal ArticleDOI
Application of support vector machine models for forecasting solar and wind energy resources: A review
TL;DR: In this article, a hybrid support vector machine (SVM) model was proposed to forecast both solar and wind energy resources for most of the locations in the United States, where the authors highlighted main problems, opportunities and future work in this research area.
Journal ArticleDOI
Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China
Junliang Fan,Xiukang Wang,Lifeng Wu,Hanmi Zhou,Fucang Zhang,Xiang Yu,Xianghui Lu,Youzhen Xiang +7 more
TL;DR: Wang et al. as discussed by the authors proposed two machine learning algorithms, i.e., Support Vector Machine (SVM) and a novel simple tree-based ensemble method named Extreme Gradient Boosting (XGBoost), for accurate prediction of daily H using limited meteorological data.
Journal ArticleDOI
A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset
TL;DR: In this article, a wavelet-coupled support vector machine (W-SVM) model was adopted to forecast global incident solar radiation based on the sunshine hours (St), minimum temperature (Tmax), maximum temperature, Tmax, Tmin, E, P, and precipitation (P) as the predictor variables.
Journal ArticleDOI
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques
TL;DR: A systematic and critical review on the methods used to forecast PV power output with main focus on the metaheuristic and machine learning methods to assist researchers in choosing the best forecasting technique for future research.
References
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TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.
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TL;DR: A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.