Abstract
Photovoltaic (PV) power intermittence impacts electrical grid security and operation. Precise PV power and solar irradiation forecasts have been investigated as significant reducers of such impacts. Predicting solar irradiation involves uncertainties related to the characteristics of time series and their high volatility due to the dependence on many weather conditions. We propose a systematic review of PV power and solar resource forecasting, considering technical aspects related to each applied methodology. Our review covers the performance analysis of various physical, statistical, and machine learning models. These methodologies should contribute to decision-making, being applicable to different sites and climatic conditions. About 42% of the analyzed articles developed hybrid approaches, 83% performed short-term prediction, and more than 78% had, as forecast goal, PV power, solar irradiance, and solar irradiation. Considering spatial forecast scale, 66% predicted in a single field. As a trend for the coming years, we highlight the use of hybridized methodologies, especially those that optimize input and method parameters without loss of precision and postprocessing methodologies aiming at improvements in individualized applications.
Issue Section:
Review Article
References
1.
Lima
, M. A. F. B.
, Carvalho
, P. C. M.
, Fernández-Ramírez
, L. M.
, and Braga
, A. P. S.
, 2020
, “Improving Solar Forecasting Using Deep Learning and Portfolio Theory Integration
,” Energy
, 195
, p. 117016
. 2.
Kaur
, A.
, Nonnenmacher
, L.
, Pedro
, H. T. C.
, and Coimbra
, C. F. M.
, 2016
, “Benefits of Solar Forecasting for Energy Imbalance Markets
,” Renewable Energy
, 86
, pp. 819
–830
. 3.
Wang
, G. C.
, Ratnam
, E.
, Haghi
, H. V.
, and Kleissl
, J.
, 2019
, “Corrective Receding Horizon EV Charge Scheduling Using Short-Term Solar Forecasting
,” Renewable Energy
, 130
, pp. 1146
–1158
. 4.
Pedro
, H. T. C.
, and Coimbra
, C. F. M.
, 2015
, “Nearest-Neighbor Methodology for Prediction of Intra-Hour Global Horizontal and Direct Normal Irradiances
,” Renewable Energy
, 80
, pp. 770
–782
. 5.
Yang
, D.
, 2018
, “SolarData: An R Package for Easy Access of Publicly Available Solar Datasets
,” Sol. Energy
, 171
, pp. A3
–A12
. 6.
Nobre
, A. M.
, Karthik
, S.
, Liu
, H.
, Yang
, D.
, Martins
, F. R.
, Pereira
, E. B.
, Rüther
, R.
, Reindl
, T.
, and Peters
, I. M.
, 2016
, “On the Impact of Haze on the Yield of Photovoltaic Systems in Singapore
,” Renewable Energy
, 89
, pp. 389
–400
. 7.
Yang
, D.
, Walsh
, W. M.
, and Jirutitijaroen
, P.
, 2014
, “Estimation and Applications of Clear Sky Global Horizontal Irradiance at the Equator
,” ASME J. Sol. Energy Eng.
, 136
(3
), p. 034505
. 8.
Yang
, D.
, and Gueymard
, C. A.
, 2019
, “Producing High-Quality Solar Resource Maps by Integrating High- and Low Accuracy Measurements Using Gaussian Processes
,” Renewable Sustainable Energy Rev.
, 113
, p. 109260
. 9.
Yang
, D.
, 2019
, “Standard of Reference in Operational Day-Ahead Deterministic Solar Forecasting
,” J. Renewable Sustainable Energy
, 11
(5
), p. 053702
. 10.
Antonanzas
, J.
, Osorio
, N.
, Escobar
, R.
, Urraca
, R.
, Martinez-de-Pison
, F. J.
, and Antonanzas-Torres
, F.
, 2016
, “Review of Photovoltaic Power Forecasting
,” Sol. Energy
, 136
, pp. 78
–111
. 11.
Liu
, D.
, and Sun
, K.
, 2019
, “Random Forest Solar Power Forecast Based on Classification Optimization
,” Energy
, 187
, p. 115940
. 12.
Mathiesen
, P.
, and Kleissl
, J.
, 2011
, “Evaluation of Numerical Weather Prediction for Intra-Day Solar Forecasting in the Continental United States
,” Sol. Energy
, 85
(5
), pp. 967
–977
. 13.
Peruchena
, C. M. F.
, Gastón
, M.
, Schroedter-Homscheidt
, M.
, Kosmale
, M.
, Marco
, I. M.
, García-Moya
, J. A.
, and Casado-Rubio
, J. L.
, 2017
, “Dynamic Paths: Towards High Frequency Direct Normal Irradiance Forecasts
,” Energy
, 132
, pp. 315
–323
. 14.
Bacher
, P.
, Madsen
, H.
, and Nielsen
, H. A.
, 2009
, “Online Short-Term Solar Power Forecasting
,” Sol. Energy
, 83
(10
), pp. 1772
–1783
. 15.
Huang
, R.
, Huang
, T.
, Gadh
, R.
, and Li
, N.
, 2012
, “Solar Generation Prediction Using the ARMA Model in a Laboratory-Level Micro-Grid
,” IEEE Third International Conference on Smart Grid Communications
, Tainan, Taiwan
, Nov. 5–8
, pp. 528
–533
.16.
Wu
, J.
, and Chan
, C. K.
, 2012
, “The Prediction of Monthly Average Solar Radiation With TDNN and ARIMA
,” 11th International Conference on Machine Learning and Applications
, Boca Raton, FL
, Dec. 12–15
, pp. 469
–474
.17.
Jallal M.
, A.
, Yassini A.
, E.
, Chabaa
, S.
, Zeroual
, A.
, and Ibnyaich
, S.
, 2020
, “AI Data Driven Approach-Based Endogenous Inputs for Global Solar Radiation Forecasting
,” Ingénierie des Systèmes d'Information
, 25
(1
), pp. 27
–34
. 18.
Hu
, K.
, Cao
, S.
, Wang
, L.
, Li
, W.
, and Lv
, M.
, 2018
, “A New Ultra-Short-Term Photovoltaic Power Prediction Model Based on Ground-Based Cloud Images
,” J. Cleaner Prod.
, 200
, pp. 731
–745
. 19.
Jiang
, H.
, Dong
, Y.
, Wang
, J.
, and Li
, Y.
, 2015
, “Intelligent Optimization Models Based on Hard-Ridge Penalty and RBF for Forecasting Global Solar Radiation
,” Energy Convers. Manage.
, 95
, pp. 42
–58
. 20.
Jung
, Y.
, Jung
, J.
, Kim
, B.
, and Han
, S.
, 2020
, “Long Short-Term Memory Recurrent Neural Network for Modeling Temporal Patterns in Long-Term Power Forecasting for Solar PV Facilities: Case Study of South Korea
,” J. Clean. Prod.
, 250
, p. 119476
. 21.
Gao
, M.
, Li
, J.
, Hong
, F.
, and Long
, D.
, 2019
, “Day-Ahead Power Forecasting in a Large-Scale Photovoltaic Plant Based on Weather Classification Using LSTM
,” Energy
, 187
, p. 115838
. 22.
Qing
, X.
, and Niu
, Y.
, 2018
, “Hourly Day-Ahead Solar Irradiance Prediction Using Weather Forecasts by LSTM
,” Energy
, 148
, pp. 461
–468
. 23.
Wang
, K.
, Qi
, X.
, and Liu
, H.
, 2019
, “Photovoltaic Power Forecasting-Based LSTM-Convolutional Network
,” Energy
, 189
, p. 116225
. 24.
Wang
, K.
, Qi
, X.
, and Liu
, H.
, 2019
, “A Comparison of Day-Ahead Photovoltaic Power Forecasting Models Based on Deep Learning Neural Network
,” Appl. Energy
, 251
, p. 113315
. 25.
Das U.
, K.
, Tey
, K. S.
, Seyedmahmoudian
, M.
, Mekhilef
, S.
, Idris
, M. Y. I.
, Deventer
, W. V.
, Horan
, B.
, and Stojcevski
, A.
, 2018
, “Forecasting of Photovoltaic Power Generation and Model Optimization: A Review
,” Renewable Sustainable Energy Rev.
, 81
, pp. 912
–928
. 26.
Kitchenham
, B.
, and Charters
, S.
, 2007
, Guidelines for Performing Systematic Literature Reviews in Software Engineering
, Department of Computer Science, University of Durham
, Durham, UK
, Technical Report EBSE-2007-01.27.
Santos
, I. S.
, Andrade
, R. M. d. C.
, Rocha
, L. S.
, Matalonga
, S.
, Oliveira
, K. M.
, and Travassos
, G. H.
, 2017
, “Test Case Design for Context-Aware Applications: Are We There yet?
,” Inf. Softw. Technol.
, 88
, pp. 1
–16
. 28.
MertYagli
, G.
, Yang
, D.
, and Srinivasan
, D.
, 2019
, “Automatic Hourly Solar Forecasting Using Machine Learning Models
,” Renewable Sustainable Energy Rev.
, 105
, pp. 487
–498
. 29.
Chu
, Y.
, and Coimbra
, C. F. M.
, 2017
, “Short-Term Probabilistic Forecasts for Direct Normal Irradiance
,” Renewable Energy
, 101
, pp. 526
–536
. 30.
Yang
, D.
, 2019
, “Making Reference Solar Forecasts With Climatology, Persistence, and Their Optimal Convex Combination
,” Sol. Energy
, 193
, pp. 981
–985
. 31.
Boilley
, A.
, Thomas
, C.
, Marchand
, M.
, Wey
, E.
, and Blanc
, P.
, 2016
, “The Solar Forecast Similarity Method: a new Method to Compute Solar Radiation Forecasts for the Next day
,” Energy Procedia
, 91
, pp. 1018
–1023
. 32.
Voyant
, C.
, Notton
, G.
, Darras
, C.
, Fouilloy
, A.
, and Motte
, F.
, 2017
, “Uncertainties in Global Radiation Time Series Forecasting Using Machine Learning: The Multilayer Perceptron Case
,” Energy
, 125
, pp. 248
–257
. 33.
Fouilloy
, A.
, Voyant
, C.
, Notton
, G.
, Motte
, F.
, Paoli
, C.
, Nivet
, M.
, Guillot
, E.
, and Duchaud
, J.
, 2018
, “Solar Irradiation Prediction With Machine Learning: Forecasting Models Selection Method Depending on Weather Variability
,” Energy
, 165
, pp. 620
–629
. 34.
Benali
, L.
, Notton
, G.
, Fouilloy
, A.
, Voyant
, C.
, and Dizene
, R.
, 2019
, “Solar Radiation Forecasting Using Artificial Neural Network and Random Forest Methods: Application to Normal Beam, Horizontal Diffuse and Global Components
,” Renewable Energy
, 132
, pp. 871
–884
. 35.
Li
, J.
, Ward
, J. K.
, Tong
, J.
, Collins
, L.
, and Platt
, G.
, 2016
, “Machine Learning for Solar Irradiance Forecasting of Photovoltaic System
,” Renewable Energy
, 90
, pp. 542
–553
. 36.
Aguiar
, L. M.
, Pereira
, B.
, Lauret
, P.
, Díaz
, F.
, and David
, M.
, 2016
, “Combining Solar Irradiance Measurements, Satellite-Derived Data, and a Numerical Weather Prediction Model to Improve Intra-Day Solar Forecasting
,” Renewable Energy
, 97
, pp. 599
–610
. 37.
Pedro
, H. T. C.
, Coimbra
, C. F. M.
, David
, M.
, and Lauret
, P.
, 2018
, “Assessment of Machine Learning Techniques for Deterministic and Probabilistic Intra-Hour Solar Forecasts
,” Renewable Energy
, 123
, pp. 191
–203
. 38.
McCandless
, T. C.
, Haupt
, S. E.
, and Young
, G. S.
, 2016
, “A Regime-Dependent Artificial Neural Network Technique for Short-Range Solar Irradiance Forecasting
,” Renewable Energy
, 89
, pp. 351
–359
. 39.
Lan
, H.
, Yin
, H.
, Hong
, Y.
, Wen
, S.
, Yu
, D. C.
, and Cheng
, P.
, 2018
, “Day-Ahead Spatio-Temporal Forecasting of Solar Irradiation Along a Navigation Route
,” Appl. Energy
, 211
, pp. 15
–27
. 40.
Ghimire
, S.
, Deo
, R. C.
, Raj
, N.
, and Mi
, J.
, 2019
, “Deep Solar Radiation Forecasting With Convolutional Neural Network and Long Short-Term Memory Network Algorithms
,” Appl. Energy
, 253
, p. 113541
. 41.
Rahmann
, C.
, Mayol
, C.
, and Haas
, J.
, 2018
, “Dynamic Control Strategy in Partially-Shaded Photovoltaic Power Plants for Improving the Frequency of the Electricity System
,” J. Cleaner Prod.
, 202
, pp. 109
–119
. 42.
MertYagli
, G.
, Yang
, D.
, Gandhi
, O.
, and Srinivasan
, D.
, 2020
, “Can We Justify Producing Univariate Machine-Learning Forecasts With Satellite-Derived Solar Irradiance?
,” Appl. Energy
, 259
, p. 114122
. 43.
Zhu
, T.
, Wei
, H.
, Zhao
, X.
, Zhang
, C.
, and Zhang
, K.
, 2017
, “Clear-Sky Model for Wavelet Forecast of Direct Normal Irradiance
,” Renewable Energy
, 104
, pp. 1
–8
. 44.
Liu
, Y.
, Qin
, H.
, Zhang
, Z.
, Pei
, S.
, Wang
, C.
, Yu
, X.
, Jiang
, Z.
, and Zhou
, J.
, 2019
, “Ensemble Spatiotemporal Forecasting of Solar Irradiation Using Variational Bayesian Convolutional Gate Recurrent Unit Network
,” Appl. Energy
, 253
, p. 113596
. 45.
Heo
, J.
, Jung
, J.
, Kim
, B.
, and Han
, S.
, 2020
, “Digital Elevation Model-Based Convolutional Neural Network Modeling for Searching of High Solar Energy Regions
,” Appl. Energy
, 262
, p. 114588
. 46.
Nirudh
, J.
, and Somchat
, J.
, 2019
, “Estimation of Solar Potential for Thailand Using Adaptive Neurofuzzy Inference System Models
,” ASME J. Sol. Energy Eng.
, 141
(6
), p. 061009
. 47.
Wen
, H.
, Du
, Y.
, Chen
, X.
, Lim
, E.
, Wen
, H.
, Jiang
, L.
, and Xiang
, W.
, 2021
, “Deep Learning Based Multistep Solar Forecasting for PV Ramp-Rate Control Using Sky Images
,” IEEE Trans. Ind. Electron.
, 17
(2
), pp. 1397
–1406
. 48.
Kamadinata
, J. O.
, Ken
, T. L.
, and Suwa
, T.
, 2020
, “Solar Irradiance Fluctuation Prediction Methodology Using Artificial Neural Networks
,” ASME J. Sol. Energy Eng.
, 142
(3
), p. 031003
. 49.
Prado-Rujas
, I.
, García-Dopico
, A.
, Serrano
, E.
, and Pérez
, M. S.
, 2021
, “A Flexible and Robust Deep Learning-Based System for Solar Irradiance Forecasting
,” IEEE Access
, 9
, pp. 12348
–12361
. 50.
Vaz
, A. G. R.
, Elsinga
, B.
, van Sark
, W. G. J. H. M.
, and Brito
, M. C.
, 2016
, “An Artificial Neural Network to Assess the Impact of Neighboring Photovoltaic Systems in Power Forecasting in Utrecht, the Netherlands
,” Renewable Energy
, 85
, pp. 631
–641
. 51.
Cervone
, G.
, Clemente-Harding
, L.
, Alessandrini
, L.
, and Monache
, L. D.
, 2017
, “Short-Term Photovoltaic Power Forecasting Using Artificial Neural Networks and an Analog Ensemble
,” Renewable Energy
, 108
, pp. 274
–286
. 52.
Hossain
, M.
, Mekhilef
, S.
, Danesh
, M.
, Olatomiwa
, L.
, and Shamshirband
, S.
, 2017
, “Application of Extreme Learning Machine for Short Term Output Power Forecasting of Three Grid-Connected PV Systems
,” J. Cleaner Prod.
, 167
, pp. 395
–405
. 53.
Han
, Y.
, Wang
, N.
, Ma
, M.
, Zhou
, H.
, Dai
, S.
, and Zhu
, H.
, 2019
, “A PV Power Interval Forecasting Based on Seasonal Model and Nonparametric Estimation Algorithm
,” Sol. Energy
, 184
, pp. 515
–526
. 54.
Alfulayyih
, Y. M.
, Li
, P.
, and Gwesha
, A. O.
, 2019
, “A Generic Algorithm for Planning the Year-Round Solar Energy Harvest/Storage to Supply Solar-Based Stable Power
,” ASME J. Sol. Energy Eng.
, 142
(4
), p. 041002
. 55.
Ameen
, A. M.
, Pasupuleti
, J.
, Khatib
, T.
, Elmenreich
, W.
, and Kazem
, H. A.
, 2015
, “Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network
,” ASME J. Sol. Energy Eng.
, 137
(4
), p. 041010
. 56.
Sridharan
, M.
, Jayaprakash
, G.
, Chandrasekar
, M.
, Vigneshwar
, P.
, Paramaguru
, S.
, and Amarnath
, K.
, 2018
, “Prediction of Solar Photovoltaic/Thermal Collector Power Output Using Fuzzy Logic
,” ASME J. Sol. Energy Eng.
, 140
(6
), p. 061013
. 57.
Yang
, D.
, 2019
, “On Post-Processing Day-Ahead NWP Forecasts Using Kalman Filtering
,” Sol. Energy
, 182
, pp. 179
–181
. 58.
Aryaputera
, A. W.
, Yang
, D.
, and Walsh
, W. M.
, 2015
, “Day-Ahead Solar Irradiance Forecasting in a Tropical Environment
,” ASME J. Sol. Energy Eng.
, 137
(5
), p. 051009
. 59.
Kurtz
, B.
, Mejia
, F.
, and Kleissl
, J.
, 2017
, “A Virtual Sky Imager Testbed for Solar Energy Forecasting
,” Sol. Energy
, 158
, pp. 753
–759
. 60.
Chow
, C. W.
, Belongie
, S.
, and Kleissl
, J.
, 2015
, “Cloud Motion and Stability Estimation for Intra-Hour Solar Forecasting
,” Sol. Energy
, 115
, pp. 645
–655
. 61.
Sahu
, D. K.
, Yang
, H.
, and Kleissl
, J.
, 2018
, “Assimilating Observations to Simulate Marine Layer Stratocumulus for Solar Forecasting
,” Sol. Energy
, 162
, pp. 454
–471
. 62.
Wu
, E.
, Clemesha
, R. E. S.
, and Kleissl
, J.
, 2018
, “Coastal Stratocumulus Cloud Edge Forecasts
,” Sol. Energy
, 164
, pp. 355
–369
. 63.
Yang
, H.
, and Kleissl
, J.
, 2016
, “Preprocessing WRF Initial Conditions for Coastal Stratocumulus Forecasting
,” Sol. Energy
, 133
, pp. 180
–193
. 64.
Lauret
, P.
, Perez
, R.
, Aguiar
, L. M.
, Tapachès
, E.
, Diagne
, H. M.
, and David
, M.
, 2016
, “Characterization of the Intraday Variability Regime of Solar Irradiation of Climatically Distinct Locations
,” Sol. Energy
, 125
, pp. 99
–110
. 65.
Wang
, Y.
, and Wu
, L.
, 2016
, “On Practical Challenges of Decomposition-Based Hybrid Forecasting Algorithms for Wind Speed and Solar Irradiation
,” Energy
, 112
, pp. 208
–220
. 66.
Cheng
, H.
, 2017
, “Cloud Tracking Using Clusters of Feature Points for Accurate Solar Irradiance Nowcasting
,” Renewable Energy
, 104
, pp. 281
–289
. 67.
Miller
, S. D.
, Rogers
, M. A.
, Haynes
, J. M.
, Sengupta
, M.
, and Heidinger
, A. K.
, 2018
, “Short-Term Solar Irradiance Forecasting via Satellite/Model Coupling
,” Sol. Energy
, 168
, pp. 102
–117
. 68.
García-Hinde
, O.
, Terrén-Serrano
, G.
, Hombrados-Herrera
, MÁ
, Gómez-Verdejo
, V.
, Jiménez-Fernández
, S.
, Casanova-Mateo
, C.
, Sanz-Justo
, J.
, Martínez-Ramón
, M.
, and Salcedo-Sanz
, S.
, 2018
, “Evaluation of Dimensionality Reduction Methods Applied to Numerical Weather Models for Solar Radiation Forecasting
,” Eng. Appl. Artif. Intell.
, 69
, pp. 157
–167
. 69.
Huva
, R.
, Verbois
, H.
, and Walsh
, W.
, 2020
, “Comparisons of Next-Day Solar Forecasting for Singapore Using 3DVAR and 4DVAR Data Assimilation Approaches With the WRF Model
,” Renewable Energy
, 147
, pp. 663
–671
. 70.
Nonnenmacher
, L.
, Kaur
, A.
, and Coimbra
, C. F. M.
, 2016
, “Day-Ahead Resource Forecasting for Concentrated Solar Power Integration
,” Renewable Energy
, 86
, pp. 866
–876
. 71.
Murata
, A.
, Ohtake
, H.
, and Oozeki
, T.
, 2018
, “Modeling of Uncertainty of Solar Irradiance Forecasts on Numerical Weather Predictions With the Estimation of Multiple Confidence Intervals
,” Renewable Energy
, 117
, pp. 193
–201
. 72.
Akarslan
, E.
, and Hocaoglu
, F. O.
, 2017
, “A Novel Method Based on Similarity for Hourly Solar Irradiance Forecasting
,” Renewable Energy
, 112
, pp. 337
–346
. 73.
Fen
, L.
, Quanquan
, Y.
, Shanxu
, D.
, Jinbin
, Z.
, Nianjun
, M.
, and Zhenghong
, C.
, 2015
, “A Novel Model for Daily Energy Production Estimation of Grid-Connected Photovoltaic System
,” ASME J. Sol. Energy Eng.
, 137
(3
), p. 031013
. 74.
Inage
, S.
, 2017
, “Development of an Advection Model for Solar Forecasting Based on Ground Data First Report: Development and Verification of a Fundamental Model
,” Sol. Energy
, 153
, pp. 414
–434
. 75.
Larson
, D. P.
, Nonnenmacher
, L.
, and Coimbra
, C. F. M.
, 2016
, “Day-Ahead Forecasting of Solar Power Output From Photovoltaic Plants in the American Southwest
,” Renewable Energy
, 91
, pp. 11
–20
. 76.
Pedro
, H. T. C.
, Lim
, E.
, and Coimbra
, C. F. M.
, 2018
, “A Database Infrastructure to Implement Real-Time Solar and Wind Power Generation Intra-Hour Forecasts
,” Renewable Energy
, 123
, pp. 513
–525
. 77.
Yang
, D.
, 2019
, “A Universal Benchmarking Method for Probabilistic Solar Irradiance Forecasting
,” Sol. Energy
, 184
, pp. 410
–416
. 78.
Aryaputera
, A. W.
, Yang
, D.
, Zhao
, L.
, and Walsh
, W. M.
, 2015
, “Very Short-Term Irradiance Forecasting at Unobserved Locations Using Spatiotemporal Kriging
,” Sol. Energy
, 122
, pp. 1266
–1278
. 79.
Yang
, D.
, Ye
, Z.
, Lim
, L. H. I.
, and Zibo
, D.
, 2015
, “Very Short-Term Irradiance Forecasting Using the Lasso
,” Sol. Energy
, 114
, pp. 314
–326
. 80.
Yang
, D.
, 2020
, “Ensemble Model Output Statistics as a Probabilistic Site-Adaptation Tool for Satellite Derived and Reanalysis Solar Irradiance
,” J. Renewable Sustainable Energy
, 12
(1
), p. 016102
. 81.
Reikard
, G.
, Haupt
, S. E.
, and Jensen
, T.
, 2017
, “Forecasting Ground-Level Irradiance Over Short Horizons: Time Series, Meteorological, and Time-Varying Parameter Models
,” Renewable Energy
, 112
, pp. 474
–485
. 82.
Voyant
, C.
, Motte
, F.
, Fouilloy
, A.
, Notton
, G.
, Paoli
, C.
, and Nivet
, M.
, 2017
, “Forecasting Method for Global Radiation Time Series Without Training Phase: Comparison With Other Well-Known Prediction Methodologies
,” Energy
, 120
, pp. 199
–208
. 83.
Reikard
, G.
, and Hansen
, C.
, 2019
, “Forecasting Solar Irradiance at Short Horizons: Frequency and Time Domain Models
,” Renewable Energy
, 135
, pp. 1270
–1290
. 84.
Akarslan
, E.
, Hocaoglu
, F. O.
, and Edizkan
, R.
, 2018
, “Novel Short Term Solar Irradiance Forecasting Models
,” Renewable Energy
, 123
, pp. 58
–66
. 85.
Dong
, J.
, Olama
, M. M.
, Kuruganti
, T.
, Melin
, A. M.
, Djouadi
, S. M.
, Zhang
, Y.
, and Xue
, Y.
, 2020
, “Novel Stochastic Methods to Predict Short-Term Solar Radiation and Photovoltaic Power+
,” Renewable Energy
, 145
, pp. 333
–346
. 86.
Fatemi
, S. A.
, Kuh
, A.
, and Fripp
, M.
, 2018
, “Parametric Methods for Probabilistic Forecasting of Solar Irradiance
,” Renewable Energy
, 129
, pp. 666
–676
. 87.
e Silva
, R. A.
, and Brito
, M. C.
, 2019
, “Spatiotemporal PV Forecasting Sensitivity to Modules’ Tilt and Orientation
,” Appl. Energy
, 255
, p. 113807
. 88.
Bin Shams
, M.
, Haji
, S.
, Salman
, A.
, Abdali
, H.
, and Alsaffar
, A.
, 2016
, “Time Series Analysis of Bahrain's First Hybrid Renewable Energy System
,” Energy
, 103
, pp. 1
–15
. 89.
Giammanco
, C.
, Pierro
, M.
, Cornaro
, C.
, and di Carlo
, A.
, 2016
, “Statistic Determination of Storage Capacity for Photovoltaic Energy Imbalance Mitigation
,” ASME J. Sol. Energy Eng.
, 138
(1
), p. 011002
. 90.
Zheng
, L.
, Liu
, Z.
, Shen
, J.
, and Wu
, C.
, 2018
, “Very Short-Term Maximum Lyapunov Exponent Forecasting Tool for Distributed Photovoltaic Output
,” Appl. Energy
, 229
, pp. 1128
–1139
. 91.
Paulescu
, M.
, Brabec
, M.
, Boata
, R.
, and Badescu
, V.
, 2017
, “Structured, Physically Inspired (Gray Box) Models Versus Black Box Modeling for Forecasting the Output Power of Photovoltaic Plants
,” Energy
, 121
, pp. 792
–802
. 92.
van der Meer
, D. W.
, Shepero
, M.
, Svensson
, A.
, Widén
, J.
, and Munkhammar
, J. W. J.
, 2018
, “Probabilistic Forecasting of Electricity Consumption, Photovoltaic Power Generation and Net Demand of an Individual Building Using Gaussian Processes
,” Appl. Energy
, 213
, pp. 195
–207
. 93.
Ni
, Q.
, Zhuang
, S.
, Sheng
, H.
, Kang
, G.
, and Xiao
, J.
, 2017
, “An Ensemble Prediction Intervals Approach for Short-Term PV Power Forecasting
,” Sol. Energy
, 155
, pp. 1072
–1083
. 94.
Yang
, D.
, Wu
, E.
, and Kleissl
, J.
, 2019
, “Operational Solar Forecasting for the Real-Time Market
,” Int. J. Forecast.
, 35
(4
), pp. 1499
–1519
. 95.
de la Rosa
, E. O.
, Nuñez
, G. B.
, Roca
, A. O. P.
, and Ledesma-Alonso
, R.
, 2019
, “An Empiric-Stochastic Approach, Based on Normalization Parameters, to Simulate Solar Irradiance
,” ASME J. Sol. Energy Eng.
, 141
(6
), p. 061011
. 96.
Li
, P.
, Bessafi
, M.
, Morel
, B.
, Chabriat
, J.
, Delsaut
, M.
, and Li
, Q.
, 2020
, “Daily Surface Solar Radiation Prediction Mapping Using Artificial Neural Network: The Case Study of Reunion Island
,” ASME J. Sol. Energy Eng.
, 142
(2
), p. 021009
. 97.
Raghavan
, V. S. G.
, and Gopalan
, H.
, 2018
, “URB-Solar: An Open-Source Tool for Solar Power Prediction in Urban Areas
,” ASME J. Sol. Energy Eng.
, 140
(6
), p. 064501
. 98.
Pedro
, H. T. C.
, and Coimbra
, C. F. M.
, 2015
, “Short-Term Irradiance Forecastability for Various Solar Micro-Climates
,” Sol. Energy
, 122
, pp. 587
–602
. 99.
Zagouras
, A.
, Pedro
, H. T. C.
, and Coimbra
, C. F. M.
, 2015
, “On the Role of Lagged Exogenous Variables and Spatiotemporal Correlations in Improving the Accuracy of Solar Forecasting Methods
,” Renewable Energy
, 78
, pp. 203
–218
. 100.
Dong
, Z.
, Yang
, D.
, Reindl
, T.
, and Walsh
, W. M.
, 2015
, “A Novel Hybrid Approach Based on Self-Organizing Maps, Support Vector Regression and Particle Swarm Optimization to Forecast Solar Irradiance
,” Energy
, 82
, pp. 570
–577
. 101.
Wang
, G. C.
, Urquhart
, B.
, and Kleissl
, J.
, 2019
, “Cloud Base Height Estimates From Sky Imagery and a Network of Pyranometers
,” Sol. Energy
, 184
, pp. 594
–609
. 102.
Yang
, D.
, Sharma
, V.
, Ye
, Z.
, Lim
, L. I.
, Zhao
, L.
, and Aryaputera
, A. W.
, 2015
, “Forecasting of Global Horizontal Irradiance by Exponential Smoothing, Using Decompositions
,” Energy
, 81
, pp. 111
–119
. 103.
Verbois
, H.
, Huva
, R.
, Rusydi
, A.
, and Walsh
, W.
, 2018
, “Solar Irradiance Forecasting in the Tropics Using Numerical Weather Prediction and Statistical Learning
,” Sol. Energy
, 162
, pp. 265
–277
. 104.
Prasad
, R.
, Ali
, M.
, Xiang
, Y.
, and Khan
, H.
, 2020
, “A Double Decomposition-Based Modelling Approach to Forecast Weekly Solar Radiation
,” Renewable Energy
, 152
, pp. 9
–22
. 105.
Aguiar
, L. M.
, Polo
, J.
, Vindel
, J. M.
, and Oliver
, A.
, 2019
, “Analysis of Satellite Derived Solar Irradiance in Islands With Site Adaptation Techniques for Improving the Uncertainty
,” Renewable Energy
, 135
, pp. 98
–107
. 106.
Hocaoglu
, F. O.
, and Serttas
, F.
, 2017
, “A Novel Hybrid (Mycielski-Markov) Model for Hourly Solar Radiation Forecasting
,” Renewable Energy
, 108
, pp. 635
–643
. 107.
Nam
, S.
, and Hur
, J.
, 2019
, “A Hybrid Spatiotemporal Forecasting of Solar Generating Resources for Grid Integration
,” Energy
, 177
, pp. 503
–510
. 108.
Prasad
, R.
, Ali
, M.
, Kwan
, P.
, and Khan
, H.
, 2019
, “Designing a Multi-Stage Multivariate Empirical Mode Decomposition Coupled With Ant Colony Optimization and Random Forest Model to Forecast Monthly Solar Radiation
,” Appl. Energy
, 236
, pp. 778
–792
. 109.
Bouzgou
, H.
, and Gueymard
, C. A.
, 2019
, “Fast Short-Term Global Solar Irradiance Forecasting With Wrapper Mutual Information
,” Renewable Energy
, 133
, pp. 1055
–1065
. 110.
Monjoly
, S.
, André
, M.
, Calif
, R.
, and Soubdhan
, T.
, 2017
, “Hourly Forecasting of Global Solar Radiation Based on Multiscale Decomposition Methods: A Hybrid Approach
,” Energy
, 119
, pp. 288
–298
. 111.
Alonso-Montesinos
, J.
, Monterreal
, R.
, Fernandez-Reche
, J.
, Ballestrín
, J.
, Carra
, E.
, Polo
, J.
, Barbero
, J.
, Batlles
, F. J.
, Lopez
, G.
, Enrique
, R.
, Martínez-Durban
, M.
, and Marzo
, A.
, 2019
, “Intra-Hour Energy Potential Forecasting in a Central Solar Power Plant Receiver Combining Meteosat Images and Atmospheric Extinction
,” Energy
, 188
, p. 116034
. 112.
Guermoui
, M.
, Melgani
, F.
, and Danilo
, C.
, 2018
, “Multi-Step Ahead Forecasting of Daily Global and Direct Solar Radiation: A Review and Case Study of Ghardaia Region
,” J. Cleaner Prod.
, 201
, pp. 716
–734
. 113.
Maleki
, A.
, Khajeh
, M. G.
, and Rosen
, M. A.
, 2016
, “Weather Forecasting for Optimization of a Hybrid Solar-Wind-Powered Reverse Osmosis Water Desalination System Using a Novel Optimizer Approach
,” Energy
, 114
, pp. 1120
–1134
. 114.
Hong
, Y.
, Martinez
, J. J. F.
, and Fajardo
, A. C.
, 2020
, “Day-Ahead Solar Irradiation Forecasting Utilizing Gramian Angular Field and Convolutional Long Short-Term Memory
,” IEEE Acess
, 8
, pp. 18741
–18753
. 115.
Raffán
, L. C. P.
, Romero
, A.
, and Martinez
, M.
, 2018
, “Solar Energy Production Forecasting Through Artificial Neuronal Networks, Considering the Föhn, North and South Winds in San Juan, Argentina
,” The 7th International Conference on Renewable Power Generation
, Lyngby, Copenhagen, Denmark
, Sept. 26–27
. 116.
Boland
, J.
, David
, M.
, and Lauret
, P.
, 2016
, “Short Term Solar Radiation Forecasting: Island Versus Continental Sites
,” Energy
, 113
, pp. 186
–192
. 117.
Alonso-Montesinos
, J.
, Polo
, J.
, Ballestrín
, J.
, Batlles
, F. J.
, and Portillo
, C.
, 2019
, “Impact of DNI Forecasting on CSP Tower Plant Power Production
,” Renewable Energy
, 138
, pp. 368
–377
. 118.
Trapero
, J. R.
, 2016
, “Calculation of Solar Irradiation Prediction Intervals Combining Volatility and Kernel Density Estimates
,” Energy
, 114
, pp. 266
–274
. 119.
Ozoegwu
, C. G.
, 2019
, “Artificial Neural Network Forecast of Monthly Mean Daily Global Solar Radiation of Selected Locations Based on Time Series and Month Number
,” J. Cleaner Prod.
, 216
, pp. 1
–13
. 120.
Heydari
, A.
, Garcia
, D. A.
, Keynia
, F.
, Bisegna
, F.
, and Santoli
, L.
, 2019
, “A Novel Composite Neural Network-Based Method for Wind and Solar Power Forecasting in Microgrids
,” Appl. Energy
, 251
, p. 113353
. 121.
Lan
, H.
, Zhang
, C.
, Hong
, Y.
, He
, Y.
, and Wen
, S.
, 2019
, “Day-Ahead Spatiotemporal Solar Irradiation Forecasting Using Frequency-Based Hybrid Principal Component Analysis and Neural Network
,” Appl. Energy
, 247
, pp. 389
–402
. 122.
del Campo-Avila
, J.
, Piliougine
, M.
, Morales-Bueno
, R.
, and Mora-Lopez
, L.
, 2019
, “A Data Mining System for Predicting Solar Global Spectral Irradiance. Performance Assessment in the Spectral Response Ranges of Thin-Film Photovoltaic Modules
,” Renewable Energy
, 133
, pp. 828
–839
. 123.
Cheng
, H.
, 2016
, “Hybrid Solar Irradiance Now-Casting by Fusing Kalman Filter and Regressor
,” Renewable Energy
, 91
, pp. 434
–441
. 124.
Lima
, F. J. L.
, Martins
, F. R.
, Pereira
, E. B.
, Lorenz
, E.
, and Heinemann
, D.
, 2016
, “Forecast for Surface Solar Irradiance at the Brazilian Northeastern Region Using NWP Model and Artificial Neural Networks
,” Renewable Energy
, 87
, pp. 807
–818
. 125.
Guermoui
, M.
, Gairaa
, K.
, Boland
, J.
, and Arrif
, T.
, 2020
, “A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study
,” ASME J. Sol. Energy Eng.
, 143
(2
), p. 020801
. 126.
Hussain
, S.
, and AlAlili
, A.
, 2016
, “Online Sequential Learning of Neural Networks in Solar Radiation Modeling Using Hybrid Bayesian Hierarchical Approach
,” ASME J. Sol. Energy Eng.
, 138
(6
), p. 061012
. 127.
Cornaro
, C.
, Bucci
, F.
, Pierro
, M.
, Del Frate
, F.
, Peronaci
, S.
, and Taravat
, A.
, 2015
, “Twenty-Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction
,” ASME J. Sol. Energy Eng.
, 137
(3
), p. 031011
. 128.
Abdel-Nasser
, M.
, Mahmoud
, K.
, and Lehtonen
, M.
, 2021
, “Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs
,” IEEE Trans. Ind. Electron.
, 17
(3
), pp. 1873
–1881
. 129.
Chu
, Y.
, Urquhart
, B.
, Gohari
, S. M. I.
, Pedro
, H. T. C.
, Kleissl
, J.
, and Coimbra
, C. F. M.
, 2015
, “Short-Term Reforecasting of Power Output From a 48 MWe Solar PV Plant
,” Sol. Energy
, 112
, pp. 68
–77
. 130.
Doubleday
, K.
, Jascourt
, S.
, Kleiber
, W.
, and Hodge
, B.
, 2021
, “Probabilistic Solar Power Forecasting Using Bayesian Model Averaging
,” IEEE Trans. Sustainable Energy
, 12
(1
), pp. 325
–337
. 131.
Pierro
, M.
, Bucci
, F.
, Felice
, M.
, Maggioni
, E.
, Perotto
, A.
, Spada
, F.
, Moser
, D.
, and Cornaro
, C.
, 2017
, “Deterministic and Stochastic Approaches for Day-Ahead Solar Power Forecasting
,” ASME J. Sol. Energy Eng.
, 139
(2
), p. 021010
. 132.
Chu
, Y.
, Pedro
, H. T. C.
, Kaur
, A.
, Kleissl
, J.
, and Coimbra
, C. F. M.
, 2017
, “Net Load Forecasts for Solar-Integrated Operational Grid Feeders
,” Sol. Energy
, 158
, pp. 236
–246
. 133.
Yang
, D.
, and Dong
, Z.
, 2018
, “Operational Photovoltaics Power Forecasting Using Seasonal Time Series Ensemble
,” Sol. Energy
, 166
, pp. 529
–541
. 134.
Wolff
, B.
, Kühnert
, J.
, Lorenz
, E.
, Kramer
, O.
, and Heinemann
, D.
, 2016
, “Comparing Support Vector Regression for PV Power Forecasting to a Physical Modeling Approach Using Measurement, Numerical Weather Prediction, and Cloud Motion Data
,” Sol. Energy
, 135
, pp. 197
–208
. 135.
Zjavka
, L.
, 2020
, “Photo-Voltaic Power Daily Predictions Using Expanding PDE Sum Models of Polynomial Networks Based on Operational Calculus
,” Eng. Appl. Artif. Intell.
, 89
, p. 103409
. 136.
Jamal
, T.
, Carter
, C.
, Schmidt
, T.
, Shafiullah
, G. M.
, Calais
, M.
, and Urmee
, T.
, 2019
, “An Energy Flow Simulation Tool for Incorporating Short-Term PV Forecasting in a Diesel-PV-Battery Off-Grid Power Supply System
,” Appl. Energy
, 254
, p. 113718
. 137.
Kushwaha
, V.
, and Pindoriya
, N. M.
, 2019
, “A SARIMA-RVFL Hybrid Model Assisted by Wavelet Decomposition for Very Short-Term Solar PV Power Generation Forecast
,” Renewable Energy
, 140
, pp. 124
–139
. 138.
Chaudhary
, P.
, and Rizwan
, M.
, 2018
, “Energy Management Supporting High Penetration of Solar Photovoltaic Generation for Smart Grid Using Solar Forecasts and Pumped Hydro Storage System
,” Renewable Energy
, 118
, pp. 928
–946
. 139.
Shireen
, T.
, Shao
, C.
, Wang
, H.
, Li
, J.
, Zhang
, X.
, and Li
, M.
, 2018
, “Iterative Multi-Task Learning for Time-Series Modeling of Solar Panel PV Outputs
,” Appl. Energy
, 212
, pp. 654
–662
. 140.
Afrasiabi
, M.
, Mohammadi
, M.
, Rastegar
, M.
, and Kargarian
, A.
, 2019
, “Multi-Agent Microgrid Energy Management Based on Deep Learning Forecaster
,” Energy
, 186
, p. 115873
. 141.
Liu
, Z.
, Li
, L.
, Tseng
, M.
, and Lim
, M. K.
, 2020
, “Prediction Short-Term Photovoltaic Power Using Improved Chicken Swarm Optimizer—Extreme Learning Machine Model
,” J. Cleaner Prod.
, 248
, p. 119272
. 142.
Eseye
, A. T.
, Lehtonen
, M.
, Tukia
, T.
, Uimonen
, S.
, and Millar
, R. J.
, 2019
, “Adaptive Predictor Subset Selection Strategy for Enhanced Forecasting of Distributed PV Power Generation
,” IEEE Acess
, 7
, pp. 90652
–90665
. 143.
Dawan
, P.
, Sriprapha
, K.
, Kittisontirak
, S.
, Boonraksa
, T.
, Junhuathon
, N.
, Titiroongruang
, W.
, and Niemcharoen
, S.
, 2020
, “Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model
,” Energies
, 13
(2
), p. 351
. 144.
Suresh
, V.
, Janik
, P.
, Rezmer
, J.
, and Leonowicz
, Z.
, 2020
, “Forecasting Solar PV Output Using Convolutional Neural Networks With a Sliding Window Algorithm
,” Energies
, 13
(3
), p. 723
. 145.
Zheng
, J.
, Zhang
, H.
, Dai
, Y.
, Wang
, B.
, Zheng
, T.
, Liao
, Q.
, Liang
, Y.
, Zhang
, F.
, and Song
, X.
, 2020
, “Time Series Prediction for Output of Multi-Region Solar Power Plants
,” Appl. Energy
, 257
, p. 114001
. 146.
Anagnostos
, D.
, Schmidt
, T.
, Cavadias
, S.
, Soudris
, D.
, Poortmans
, J.
, and Catthoor
, F.
, 2019
, “A Method for Detailed, Short-Term Energy Yield Forecasting of Photovoltaic Installations
,” Renewable Energy
, 2019
(130
), pp. 122
–129
. 147.
Schmelas
, M.
, Feldmann
, T.
, Fernandes
, J. C.
, and Bollin
, E.
, 2015
, “Photovoltaics Energy Prediction Under Complex Conditions for a Predictive Energy Management System
,” ASME J. Sol. Energy Eng.
, 137
(3
), p. 031015
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