Hourly global solar forecasting models based on a supervised machine learning algorithm and time series principle

Abstract:In this paper, predictive models of hourly global solar radiation (HGSR) at one hour step ahead have been developed by adopting a new methodology. It consists on the association of a super...

[1]  Dimitris Papamichail,et al.  Solar radiation estimation methods using ANN and empirical models , 2019, Comput. Electron. Agric..

[2]  Carlo Renno,et al.  ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building , 2016 .

[3]  Adel Mellit,et al.  Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification , 2018, Renewable Energy.

[4]  J. Kleissl,et al.  Chapter 8 – Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation , 2013 .

[5]  Dalibor Petkovic,et al.  Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique , 2017, Knowledge and Information Systems.

[6]  C. Gueymard Clear-sky irradiance predictions for solar resource mapping and large-scale applications: Improved validation methodology and detailed performance analysis of 18 broadband radiative models , 2012 .

[7]  De-ti Xie,et al.  Estimation of monthly solar radiation from measured temperatures using support vector machines – A case study , 2011 .

[8]  Shahaboddin Shamshirband,et al.  Evaluation of modulation transfer function of optical lens system by support vector regression methodologies A comparative study , 2014 .

[9]  Adel Mellit,et al.  Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate , 2016 .

[10]  Tegoeh Tjahjowidodo,et al.  Adaptive neuro-fuzzy inference system for deburring stage classification and prediction for indirect quality monitoring , 2018, Appl. Soft Comput..

[11]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[12]  Ammar Ben Brahim,et al.  A Global Solar Radiation Model for the Design of Solar Energy Systems , 2008 .

[13]  Shengjun Wu,et al.  Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration , 2013 .

[14]  Gilles Notton,et al.  Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model , 2014 .

[15]  G. Notton,et al.  Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components , 2019, Renewable Energy.

[16]  D. Djafer,et al.  Estimation of atmospheric turbidity over Ghardaïa city , 2013 .

[17]  D. Mckay,et al.  Estimating solar irradiance and components , 1982 .

[18]  Tamer Khatib,et al.  A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm , 2017 .

[19]  Cyril Voyant,et al.  Hybrid methodology for hourly global radiation forecasting in Mediterranean area , 2012, ArXiv.

[20]  R. Inman,et al.  Solar forecasting methods for renewable energy integration , 2013 .

[21]  Jian Hou,et al.  Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes , 2016, Neurocomputing.

[22]  Cyril Voyant,et al.  Optimization of an artificial neural network dedicated to the multivariate forecasting of daily glob , 2011 .

[23]  B. Sivaneasan,et al.  Solar Forecasting using ANN with Fuzzy Logic Pre-processing , 2017 .

[24]  S. N. Alamri,et al.  ANN-based modelling and estimation of daily global solar radiation data: A case study , 2009 .

[25]  Cyril Voyant,et al.  Forecasting of preprocessed daily solar radiation time series using neural networks , 2010 .

[26]  Jean-Laurent Duchaud,et al.  Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability , 2018, Energy.

[27]  Miguel-Ángel Manso-Callejo,et al.  Forecasting short-term solar irradiance based on artificial neural networks and data from neighboring meteorological stations , 2016 .

[28]  Shahaboddin Shamshirband,et al.  Sensor Data Fusion by Support Vector Regression Methodology—A Comparative Study , 2015, IEEE Sensors Journal.

[29]  Gilles Notton,et al.  Estimation of hourly global solar irradiation on tilted planes from horizontal one using artificial neural networks , 2012 .

[30]  Shahaboddin Shamshirband,et al.  Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission , 2014 .

[31]  T. Muneer 2 – Daily Irradiation , 2004 .

[32]  Christian A. Gueymard,et al.  Critical analysis and performance assessment of clear sky solar irradiance models using theoretical and measured data , 1993 .

[33]  Ricardo Aler,et al.  A short-term solar radiation forecasting system for the Iberian Peninsula. Part 2: Model blending approaches based on machine learning , 2020 .

[34]  Dalibor Petković,et al.  Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms , 2017 .

[35]  Giorgio Sulligoi,et al.  A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks , 2016 .

[36]  M. Iqbal,et al.  EXTRATERRESTRIAL SOLAR IRRADIATION , 1983 .

[37]  Adel Mellit,et al.  Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study , 2012 .

[38]  Ricardo Nicolau Nassar Koury,et al.  Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms , 2018 .

[39]  Ainuddin Wahid Abdul Wahab,et al.  Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission , 2014 .