Abstract
In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problemsolving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.
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Abbreviations
- ACF:
-
Autocorrelation function
- ACO:
-
Ant colony optimization
- AIC:
-
Akaike information criteria
- ALHM:
-
Adaptive learning hybrid model
- ALSTM:
-
Attention mechanism with multiple LSTM
- ANFIS:
-
Adaptive neuro-fuzzy inference system
- ANN:
-
Artificial neural network
- APE:
-
Absolute percentage error
- AR:
-
Auto regression
- ARIMA:
-
Auto regressive integrated moving average
- ARIMAX:
-
Auto-regressive integrated moving average model with exogenous variable
- ARMA:
-
Auto regression and movie average
- AVM:
-
Atmospheric motion vectors
- BIC:
-
Bayesian information criteria
- BR:
-
Bayesian regularization
- BRT:
-
Boosted regression trees
- BNI:
-
Beam normal irradiance
- CART:
-
Classification and regression trees
- CDER:
-
Renewable energies development centre
- CDSWR:
-
Clear sky down welling short wave radiation
- CGP:
-
Pola-Ribiere conjugate gradient
- CNFRRM:
-
Cooperative network for renewable resources measurement
- CNN:
-
Convolution neural network
- CNQR:
-
Copula-based nonlinear quantile regression
- CRPSS:
-
Continuous ranked probability skill score
- CSRIO:
-
Commonwealth scientific and industrial research organization
- DL:
-
Deep learning
- DFT:
-
Discrete Fourier transform
- DGSR:
-
Daily global solar radiation
- DHI:
-
Direct horizontal irradiance
- DHR:
-
Dynamic harmonic regression
- DNI:
-
Direct normal irradiance
- DNN:
-
Deep neural network
- DSI:
-
Diffuse solar irradiance
- DSR:
-
Daily solar radiation
- DT:
-
Decision trees
- ECMWF:
-
European centre for medium-range weather forecasts
- EEMD:
-
Ensemble empirical mode decomposition
- ELM:
-
Extreme learning machine
- ELNN:
-
Elman neural network
- EMD:
-
Empirical mode decomposition
- ESR:
-
Extraterrestrial solar radiation
- FF:
-
Firefly algorithm
- FFBP:
-
Feed forward back propagation
- FOA:
-
Fruit fly optimization algorithm
- FS:
-
Forecast skill
- GA:
-
Genetic algorithm
- GABP:
-
Genetic algorithm back propagation neural network
- GBDT:
-
Gradient boosting decision trees
- GDX:
-
Gradient descent with adaptive learning rates and momentum
- GFS:
-
Global forecast system
- GHI:
-
Global horizontal irradiance
- GMDHNN:
-
Group method of data handling neural network
- GMDH:
-
Group method of data handling
- GPI:
-
Global performance indicator
- GRU:
-
Gate recurrent unit
- GSI:
-
Global solar irradiance
- GSR:
-
Global solar radiation
- HGWO:
-
Differential evolution grey wolf optimize
- HIS:
-
Hybrid intelligent system
- HMM:
-
Hidden Markov model
- ICP:
-
Interval coverage probability
- IEA:
-
International energy agency
- IMD:
-
Indian Meteorological Department
- K-NN:
-
K-nearest neural network
- KSI:
-
Kolonogorov-Smirnov integral
- LLF:
-
Log-likelihood function
- LASSO:
-
Least absolute shrinkage and selection operator
- LR:
-
Linear regression
- LM:
-
Levenberg-Marquardt
- LMBP:
-
Levenberg Marquardt back propagation
- LSTM:
-
Long short-term memory
- LS-SVM:
-
Least square support vector machine
- MABE:
-
Mean absolute biased error
- MAD:
-
Mean absolute deviation
- MAE:
-
Mean absolute error
- MAID:
-
Mean absolute interval deviation
- MAPE:
-
Mean absolute percentage error
- MBD:
-
Mean bias deviation
- MBE:
-
Mean bias error
- MARS:
-
Multivariate adaptive regression splines
- MFOA:
-
Modified fruit fly optimization
- ML:
-
Machine learning
- MLFFN:
-
Multilayer feed-forward neural network
- MLP:
-
Multi-layer perceptron
- MLR:
-
Multi linear regression
- MNRE:
-
Ministry of New and Renewable Energy
- MOS:
-
Model output statistics
- MRE:
-
Mean relative error
- MTM:
-
Markov transition method
- NAR:
-
Nonlinear autoregressive
- NCEP:
-
National Centers for Environmental Prediction
- NCMRWF:
-
National Center for Medium Range Weather Forecasting
- nE:
-
Normalized error
- nMAE:
-
Normalized mean absolute error
- NMSC:
-
National Meteorological Satellite Center
- NNE:
-
Neural network ensemble
- NNFOA:
-
Neural network modified fruit fly optimization
- nRMSE:
-
Normalized root mean square error
- NSE:
-
Nash-Sutcliffe efficiency
- NWP:
-
Numerical weather prediction
- OCCUR:
-
Optimized cross-validated clustering
- PACF:
-
Partial autocorrelation function
- PCA:
-
Principal component analysis
- PEV:
-
Potential economic value
- PINAW:
-
Prediction interval normalized average width
- PSO:
-
Particle swarm optimization
- PV:
-
Photo voltaic
- RB:
-
Batch training with bias and weight learning rules
- RBF:
-
Radial basis function
- RDI:
-
Ramp detection index
- RF:
-
Random forest
- RM:
-
Ramp magnitude
- RS:
-
Random subspace
- RSM:
-
Response surface method
- RVFL:
-
Random vector functional link
- SARIMA:
-
Seasonal auto regressive integrated moving average
- SCADA:
-
Supervisory control and data acquisition
- SCG:
-
Scaled conjugate gradient
- SP:
-
Smart persistence
- SRSCAD:
-
Square root smoothly clipped absolute deviation
- SVM:
-
Support vector machine
- TIC:
-
Theil inequality coefficient
- TMLM:
-
Time-varying multiple linear model
- TSRY:
-
Typical solar radiation year
- TMY:
-
Typical meteorological year
- WD:
-
Wavelet decomposition
- WGPR:
-
Weighted Gaussian process regression
- WGPR-CFA:
-
Weighted Gaussian process regression — cascade forecasting architecture
- WGPR-PFA:
-
Weighted Gaussian process regression — parallel forecasting architecture
- WI:
-
Wilmot’s index
- WMIM:
-
Wrapper mutual information methodology
- WRF:
-
Weather research and forecasting
- WT:
-
Wavelet transform
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Singla, P., Duhan, M. & Saroha, S. A comprehensive review and analysis of solar forecasting techniques. Front. Energy 16, 187–223 (2022). https://doi.org/10.1007/s11708-021-0722-7
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DOI: https://doi.org/10.1007/s11708-021-0722-7