Abstract
Solar irradiation data are imperatively required for any solar energy-based project. The non-accessibility and uncertainty of these data can greatly affect the implementation, management, and performance of photovoltaic or thermal systems. Developing solar irradiation estimation and forecasting approaches is an effective way to overcome these issues. Practically, prediction approaches can help anticipate events by ensuring good operation of the power network and maintaining a precise balance between the demand and supply of the power at every moment. In the literature, various estimation and forecasting methods have been developed. Artificial Neural Network (ANN) models are the most commonly used methods in solar irradiation prediction. This paper aims to firstly review, analyze, and provide an overview of different aspects required to develop an ANN model for solar irradiation prediction, such as data types, data horizon, data preprocessing, forecasting horizon, feature selection, and model type. Secondly, a highly detailed state of the art of ANN-based approaches including deep learning and hybrid ANN models for solar irradiation estimation and forecasting is presented. Finally, the factors influencing prediction model performances are discussed in order to propose recommendations, trends, and outlooks for future research in this field.
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04 February 2023
A Correction to this paper has been published: https://doi.org/10.1007/s11356-023-25750-x
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Naima El-Amarty: Conceptualization, Writing—original draft, Methodology, Writing—review & editing.
Manal Marzouq: Conceptualization, Writing—original draft, Methodology, review and Validation.
Hakim El Fadili: Conceptualization, Writing—original draft, Methodology, Supervision, review and Validation.
Saad Dosse Bennani: Conceptualization, Investigation, Supervision, Validation.
Antonio Ruano: Conceptualization, Investigation, Supervision, Validation.
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Appendix
Appendix
Nomenclature | |||
|---|---|---|---|
ACRT | ACR data logger temperature | DA | Dragonfly Algorithm |
ANFIS | Adaptive neuro fuzzy inference system | DBT | Dry bulb temperature |
Alt | Altitude | E | Earth skin temperature |
\(\propto\) | Angle of inclination | ESN | Echo State Network ( Recurrent Neural Network (RNN)) |
ANN | Artificial Neural Networks | EMD | Empirical Mode Decomposition |
P | Atmospheric pressure | EEMD | Ensemble empirical mode decomposition |
ARIMA | Auto regressive integrated moving average models | Evap | Evaporation |
AR | Auto regressive | EATE | Evolutionary algorithms with tournament selection and elitism |
ARMA | Autoregressive and moving average model | ETS | Exponentiall trend smoothing |
ARX | Autoregressive exogenous | \({H}_{o}\) | Extraterrestrial radiation \((KW.hour/{m}^{2}.D)\) |
AA | Average airmass | ELM | Extreme Learning Machine |
Azm | Average/Mean azimuth angle | FFNN | Feedforward Neural Network |
DPTm | Average dew point temperature | F(Tm) | Function of Tm |
Az | Azimuth angle | FCM | Fuzzy c-means algorithm |
PWSm | Average peak wind speed | GPV | Gaussian process vector |
RFm | Average/Mean rainfall | GRU | Gated recurrent unit |
RHm | Average relative humidity | GRNN | Generalized regression neural network |
ART | Average roof temperature | GA | Genetic algorithm |
SDm | Average/Mean sunshine duration | GP | Genetic programming |
Tm | Average/ Mean temperature | GHI | Global horizontal solar irradiation |
WCTm | Average wind chill temperature | BNI | Global normal irradiation |
WSm | Average/Mean wind speed | GTI | Global Tilted Irradiation/Irradiance |
\({\theta }_{Zm}\) | Average zenith angel | GBMs | Gradient boosting machines |
ANFIS-muSG | ANFIS-SSA-GOA | GOA | Grasshopper Optimization Algorithm |
BPNN | Backpropagation neural network | GWO | Grey Wolf Optimizer |
BSRN | Baseline Surface Radiation Network | H | Hours |
Bi-LSTM | Bidirectional long short term memory | \(\omega\) | Hour angle |
BDT | Boosted decision tree | \({CI}_{h}\) | Hourly clearness index |
Cs | Clear-sky | Dh | Hours of day |
\({K}_{t}\) | Clearness index | KELM | Kernel extreme learning machine |
CC | Cloud cover | KNN | k-Nearest Neighbours |
CI | Cloud index | Lat | Latitude |
Comp | Component | LM | Levenberg–Marquardt |
CEEMDAN | Complete ensemble empirical mode decomposition adaptive noise | Long | Longitude |
CNN | Convolutional neural network | LSTM | Long short-term memory |
DD | Day duration | ME | Maximum elevation (ME) |
\({GI}_{d}\) | Daily values of total global radiation | Pm | Mean station level pressure |
D | Day | MEA | Mind evolutionary algorithm |
DoM | Day of month | Pmin | Minimum pressure |
δ | Declination angle | Mth | Month |
DPT | Dew point temperature | MoY | Month of the year |
\(\Delta T\) | Difference of daily maximum and minimum temperature | MLP | Multilayer perceptron |
DHI | Diffuse horizontal irradiation | NREL | National Renewable Energy Laboratory |
DFT | Discrete Fourier transform | Pmax | Maximum pressure |
DWT | Discrete wavelet transformation | SDmax | Maximum sunshine duration |
DSN | Distance from Solar Noon | Tmax | Maximum temperatures |
MOSMLP | Model Output Statistics multilayer perceptron based on ANN and Numerical Weather Prediction | SAN | Simulated annealing |
Tmin | Minimum temperatures | SC | Sky cover |
NAR | Nonlinear autoregressive neural network | SP | Smart persistence |
N | Number | SA | Solar altitude angle (α) |
PSO | Particle swarm optimization | GI | Solar irradiation/irradiance/ radiation |
Per | Persistence | STMLP | Statistical model based on multilayer perceptron |
P | Pressure | SD | Sunshine duration |
PCA | Principal component analysis | SR | Sunshine Ratio |
PUNN | Product Unit Neural Network | SDday | Sunshine duration per day (hour) |
RBF | Radial basis function | SVM | Support vector machine |
RBFNN | Radial Basis Function Neural Network | Ta | Temperature ambient |
RF | Rainfall | BNIth | Theoretical Global normal irradiation |
RFR | Random forest regression | SDth | Theoretical sunshine duration |
RNN | Recurrent neural network | Td | Time of day |
VIS 0.6 and VIS 0.8 | Reflectivity | TB K-means | Transformation based K-means algorithm |
RT | Regression trees | UV | Ultraviolet index |
FOS-ELM | Regularized online sequential extreme learning machine with variable forgetting factor | VPD | Vapor pressure deficiency |
RHmax | Relative humidity Maximum | Pwv | Water vapor pressure |
RHmin | Relative humidity minimum | WNN | Wavelet neural network |
RP | Relative position among target and chosen locations | WPD | Wavelet Packet Decomposition |
RBP | Resilient back propagation | WT | Weather type |
SSA | Salp Swarm Algorithm | WD | Wind direction |
SCG | Scaled conjugate gradient | WS | Wind speed |
SaDE-ELM | Self-adaptive differential evolutionary ELM | \({\theta }_{Z}\) | Zenith angle |
SUNN | Sigmoidal Unit Neural Network | LSR | Linear least square regression |
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El-Amarty, N., Marzouq, M., El Fadili, H. et al. A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trends. Environ Sci Pollut Res 30, 5407–5439 (2023). https://doi.org/10.1007/s11356-022-24240-w
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DOI: https://doi.org/10.1007/s11356-022-24240-w