Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction
Abstract
:1. Introduction
- This review work intends to give a clear and detailed understanding of different forecasting models used for solar radiation prediction and forecasting.
- It drafts a systematic understanding of the selection and application scopes of the various forecasting models. The forecasting models are classified into eight categories.
- The tabular literature summaries were made, which will provide a synopsis of the overall features of most of the significant research work developed in solar forecasting models. It also elaborates on details of various feature reduction techniques.
- The physical models, time series models, machine learning models, deep learning models, special artificial intelligence models, probabilistic models and hybrid and ensemble models, including the basic reference model, i.e., the persistence model, are the eight models explored in our discussion.
2. Classification of Forecasting Methods
2.1. Time Horizon
2.2. Spatial Resolution
2.3. Forecast Theme
2.4. Weather Factors
- Effect of primary weather elements determined from various PV analytical models and their contributions to the solar power forecast.
- Forecast of solar power ramping events caused by unexpected weather changes.
3. Survey on Solar Irradiance and Power Forecasting Models
3.1. Survey on Persistence Models
3.2. Survey on Physical Models
3.3. Survey on Time Series Models
3.4. Survey on Machine Learning Models
3.5. Survey on Deep Learning Models
3.6. Survey on Special Artificial Intelligence Models
3.7. Survey on Hybrid and Ensemble Models
4. Statistical Metrics for Solar Power Forecasting
4.1. Pearson’s Correlation Coefficient ()
4.2. Root Mean Squared Error (RMSE)
4.3. Normalized Root Mean Squared Error (NRMSE)
4.4. Maximum Absolute Error (MaxAE)
4.5. Mean Absolute Error (MAE)
4.6. Mean Absolute Percentage Error (MAPE)
4.7. Mean Bias Error (MBE)
4.8. Kolmogorov–Smirnov Test Integral (KSI)
4.9. Confusion Matrix (CM)
4.10. Accuracy
4.11. Precision
4.12. Recall
4.13. Forecast Score
4.13.1. Score
4.13.2. Score
5. Solar Irradiance and Power Forecasting Methodologies
5.1. Persistence Model
Reference | Year | Model | Location | Forecast Horizon | Data | Conclusion | Analysis |
---|---|---|---|---|---|---|---|
Yang et al. [52] | 2012 | Persistence | Orlando and Miami, USA | 1 h ahead | Orlando 2005 October, and Miami 2004 December | RMSE value of 156.81 W/m2 in Miami 160.61 W/m2 in Orlando | Features can be further added from specific to tropical climates to improve forecasting. |
Voyant et al. [53] | 2012 | Persistence | Mediterranean, France | 1 h ahead | 6 years data | Average nRMSE is 26.2% | Complex and costly to implement in real time Gid connected systems |
Marquez et al. [54] | 2013 | Persistence | Davis and Merced, USA | 30, 60, 90, and 120 min ahead | 1 year, (1 January 2011 to 6 June 2011 and 23 November 2011 to 31 January 2012) | RMSE value of 61.24 to 107.47 W/m2 | Low importance to the ANN architecture optimization analysis and to lag feature selection process. |
5.1.1. Persistence Model 1
5.1.2. Persistence Model 2
5.1.3. Smart Persistence Model
5.2. Physical Model
5.3. Time-Series-Based Forecast Models
5.4. Machine Learning Models
5.4.1. Supervised Learning
5.4.2. Unsupervised Learning
5.4.3. Reinforcement Learning
5.4.4. Semi-Supervised Learning
5.5. Deep Learning Models
- Deep multilayer perceptron.
- Convolutional neural networks.
- Recurrent neural networks.
- Auto encoder (AE).
- Restricted Boltzmann Machine (RBM).
- Self-Organizing Maps (SOM).
5.5.1. Supervised Deep Learning
5.5.2. Unsupervised Deep Learning
5.6. Probabilistic Models
5.7. Special AI Models
5.8. Hybrid & Ensemble Machine Learning Models
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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---|---|---|---|
S. Sreekumar et al. [11] | Solar power prediction models: classification based on time horizon, input, output and application | 2018 | Presents the classification of solar power forecast models majorly by type of inputs |
Priya Gupta et al. [12] | PV power forecasting based on data-driven models: a review | 2021 | Presents the classification of solar power forecast models based on theme i.e., direct forecasting and indirect forecasting |
J. Antonanzas et al. [13] | Review of photovoltaic power forecasting | 2016 | Presents the classification of solar power forecast models based on spatial region with single and regional solar power forecasts. |
Muhammad Naveed Akhter et al. [7] | Review on forecasting of photovoltaic power generation based on machine learning and meta-heuristic techniques. | 2019 | Presents the classification of solar power forecast models based on time horizon of forecast. |
Actual Values | |||
---|---|---|---|
T/F | 1 | 0 | |
Predicted values | 1 | TP | FP |
0 | FN | TN |
Reference | Year | Model | Location | Forecast horizon | Data | Conclusion | Analysis |
---|---|---|---|---|---|---|---|
Yeom et al. [61] | 2019 | Kawamura | Korea | 1 h ahead | April 2011 to December 2017 | RMSE of 91.79 W/m2 | Misclassified results affect the forecast performance of solar radiation |
Garniwa et al. [62] | 2021 | Beyer | Seoul, Korea | 1 h ahead | 2018 year data | RMSE of 118.95 W/m2 | LSTM performs well than physical model |
Garniwa et al. [62] | 2021 | Perez | Seoul, Korea | 1 h ahead | 2018 year data | RMSE of 89.67 W/m2 | LSTM performs well than physical model |
Pereira et al. [63] | 2019 | NWP | Evora and Sines, Portugal | 1 h ahead | 2015 year data | RMSE = 57.8–164.4 W/m2 based on sky condition | Increase in data can further improve forecast performance. |
Mathiesen et al. [64] | 2013 | NWP | USA | 1 h to 1 day ahead | Hourly GHI from the SURFRAD network | rMBE 17.8% and rMAE 25.4% | Based on the cloud parameters, resolution and ramp rate, the result can be further improved |
Alfredo et al. [65] | 2012 | NWP | Spain | 6 to 39 h ahead | 362 days (2 June 2007 to 27 May 2008) | RMSE error of 11.79% of rated power output | Addition of new input parameters in the third module may increase further performance |
Reference | Year | Model | Location | Forecast Horizon | Data | Conclusion | Analysis |
---|---|---|---|---|---|---|---|
Moreno-Munoz et al. [67] | 2008 | Auto Regressive | south Spain | 5 min ahead | 4 years data, (1994–1997) | Best Fit: 65% | The use of AI models enhance better prediction performance |
Y. Li et al. [68] | 2014 | Moving average | Coloane island, Macau | 1 day ahead | 1 January 2011 to 30 June 2012. | RMSE value of 196.22 W/m2 | Analysis of cloud further enhance the performance |
Bacher et al. [69] | 2009 | ARX | Small village in Denmark | Up to 36 h ahead | 1 year data | RMSE improvement of 35% in ARX model over naïve predictor model. | Further forecast can be improved with other Time series and AI models Analysis of cloud further enhance the performance |
Y. Li et al. [68] | 2014 | ARIMA | Coloane island of Macau | 1 day ahead | 1 January 2011 to 30 June 2012. | RMSE value of 171.73 W/m2 | Analysis of cloud further enhance the performance |
Yang et al. [52] | 2012 | ARIMA | Orlando and Miami, USA | 1 h ahead | Orlando 2005 October, and Miami 2004 December | RMSE value of 29.73 W/m2 in Miami and 32.80 W/m2 in Orlando | Features can be further added from specific to tropical climates to improve forecasting. |
Y. Li et al. [68] | 2014 | ARMAX | Coloane island of Macau | 1 day ahead | 1 January 2011 to 30 June 2012. | RMSE value of 125.84 W/m2 | Analysis of cloud further enhance the performance |
Ricardo et al. [70] | 2015 | VAR | Evora, Portugal | Six hours ahead | 1 February 2011 and 6 March 2013 | Improvement of 8% to 1.5% over AR model | The algorithms like GA, PCA for future selection can achieve better performance. |
Ricardo et al. [70] | 2015 | VARX | Evora, Portugal | Six hours ahead | 1 February 2011 and 6 March 2013 | Improvement of 10% to 5.5% over AR model. | Addition of Weather station and NWP data enhance prediction accuracy. |
Ines et al. [71] | 2017 | NARX | North of Barcelona | Any time | 1 year 2010 | RMSE value of 18.64% | The results should be compared with high solar radiation fluctuations. |
Piazza et al. [72] | 2016 | NARX | Palemo, Silicy, Italy | 1 h ahead | 2002 to 2008 | nRMSE value of 6.1% | The exogeneous variable has to be changed to new parameter from temperature to increase accuracy. |
Voyant et al. [73] | 2014 | ARMA | Mediterranean, France | 24 h ahead | 10 years data | nRMSE ranges from 28.6 to 32.8% | The use of exogenous input increases the performance. Additionally, the deep and machine learning models can be applied to improve the result. |
Reference | Year | Model | Location | Forecast Horizon | Data | Conclusion |
---|---|---|---|---|---|---|
Aslam et al. [97] | 2020 | FFNN | Seoul/Korea | Hourly | 2000 to 2017 | RMSE of 109.11 W/m2 |
Mohammadi et al. [98] | 2015 | SVM | Bandar Abbas, Iran | Daily and Monthly ahead | 1992–2005 | MAPE = 3.2601–6.9996% |
SANJARI et al. [99] | 2017 | ANN | Australia | 15-min ahead | Two year data 2014 and 2015 | CRPS score = 3.81 |
Marquez et al. [54] | 2013 | ANN | Davis and Merced, USA | 30, 60, 90, and 120 min ahead | 1 year (1 January 2011 to 31 January 2012) | RMSE value of 55 to 80 W/m2 |
Torres et al. [100] | 2019 | SVR | Oklahoma, USA | 3 h ahead | 1994 to 2007 | MAE = 2225.2 KJ |
Wang, J et al. [101] | 2018 | GBDT | Oregon, USA | 1 day ahead | Random 240 day data from the 2015 and 2016 years. | nRMSE varies from 6.96 to 7.72% based on monthly test data |
Torres et al. [100] | 2019 | XGB | Oklahoma, USA | 3 h ahead | 1994 to 2007 | MAE = 2190.9 KJ |
Yap et al. [102] | 2012 | Linear regression | Darwin, Australia | 1 h ahead | 2008 to 2010 | RMSE of 6.72% |
Benali et al. [103] | 2019 | Random Forest | Odeillo, France | hourly | 3 years | nRMSE of 19.65% to 27.78% |
Liu et al. [104] | 2020 | SVM | 80 sites in China | Daily | 1957–2017 | = 0.613–0.933 for different sites |
Jimenez Perez et al. [105] | 2016 | EM model | Malaga, Spain | Hourly | 2010–2013 | rMABE = 15.2% |
Basaran et al. [106] | 2019 | EM model | Afyon, Agri, Sinop, and Hakkari in Turkey | Hourly data | 2012–2016 | RMSE varies from 4.6–14.6% |
Sun et al. [107] | 2018 | K-means and LSSVM | Beijing, China | Day ahead | 2009–2015 | MAPE 3.27% to 4.65% from single to multi-step |
Bae et al. [108] | 2017 | SVM RBF | Daejeon, South Korea | 1 h ahead | 26 months (January 2012 to April 2014) | RMSE = (49.26–62.57) W/m2 |
Reference | Year | Model | Location | Forecast horizon | Data | Conclusion |
---|---|---|---|---|---|---|
Voyant et al. [73] | 2014 | MLP | Mediterranean, France | 24 h ahead | 10 years data | nRMSE ranges from 28.6 to 31.9% |
F. Wang, et al. [115] | 2020 | BPNN | Nevada. | Day-ahead | 2011 to 2016 | RMSE of 10.31% |
C. Fang et al. [123] | 2020 | CNN | Golden, Colorado, USA | 10 min ahead | Ten years data 1 January 2008 to 31 December 2017 | RMSE of 80.14 W/m2 |
Yuchi Sun et al. [124] | 2019 | CNN | USA | 15 min ahead | 1 year (March 1st 2017 to March 1st 2018) | RMSE: 2.1 kW/25 kW |
S. Mishra et al. [125] | 2018 | RNN | Boulder, Desert Rock, Fort Peck, Sioux Falls, Bondville, Goodwin Creek, and Penn State | 1, 2, 3 and 4 h ahead | 2009, 2010, 2011, 2015, 2016 and 2017 year data | Mean RMSE of 9.713 to 39.812% |
Yu et al. [126] | 2019 | LSTM | Atlanta, New York, and Hawaii in USA. | 1 h ahead | 2013 to 2017 | RMSE in a range of 45.84 W/m2 and 41.37 W/m2 in two different locations. |
Qing et al. [127] | 2018 | LSTM | Santiago, Cape Verde. | 1 h ahead | 2.5 years (March 2011 to August 2012 and January 2013 to December 2013) | RMSE value of 76.245 W/m2 |
Chandola et al. [128] | 2020 | LSTM | Arid zones of India | 3, 6, 24 h ahead | Five years dataset (2010 to 2014) | MAPE values ranging 6.79% to 10.47%. |
Jeon and Kim [129] | 2020 | LSTM | Korea Meteorological Administration. | 24 h ahead | 1825 days | RMSE of 30 W/m2 |
Obiora et al. [130] | 2020 | LSTM | Johannesburg city | 1 h ahead | Ten years data 2009 and 2019 | Improvement of 3.2% NRMSE over the SVR model |
Mukherjee et al. [131] | 2018 | LSTM | Kharagpur, India | 1 h ahead | Fifteen years of recorded data from 2000 to 2014 | RMSE value of 57.249 W/m2 |
Justin et al. [132] | 2020 | LSTM | Weather station, Rizal | Any time | Six months data (September 2019 to February 2020) | value 0.953 and MAE value 41.738 W/m2 |
A. Rai et al. [133] | 2021 | GRU | New Delhi, India | 24 h, 48 h, and 360 h | 31-December–2015 to 31–December–2016 | MAE of 0.0321, 0.0332 and 0.0377 |
Reference | Year | Model | Location | Forecast Horizon | Data | Conclusion |
---|---|---|---|---|---|---|
M. Russo et al. [68] | 2014 | Genetic Algorithm | ENEL Catania site, Italy | 15 min | 1 whole year 2010 | RMSE: 67.6 W/1000 W |
S.Garg et al. [137] | 2020 | Markov Chains | Bhadla, Jodhpur, Rajasthan, India | Day ahead | 5 years (2010–2014) | MAPE value of 5.04 to 26.56 varies from month to month. |
V. Gunasekaran et al. [91] | 2021 | Genetic Algorithm | Bondville IL, Pennstate, PA and Desertrock, NV. | 1 min. ahead GHI | 2018 to 2020 | MAE of 4.64, 3.08 and 4.58 respectively |
Yona et al. [138] | 2013 | Fuzzy Logic | Okinawa, Japan | 24 h ahead | 1 year of data | Average MAE of 0.22 |
Reference | Year | Model | Location | Forecast Horizon | Data | Conclusion |
---|---|---|---|---|---|---|
Mitrentsis et al. [144] | 2021 | Natural Gradient Boosting | Germany | day-ahead | February 2018 to October 2019 | RMSE of 5.77 to 6.17% from reduced to full features |
S. Alessandrini et al. [145] | 2015 | Quantile Regression | Milano, Catania, and Calabria in Italy | 0–72-h ahead | January 2010 to December 2011 (Catania), July 2010 to December 2011 (Milano), and April 2011 to March 2013 (Calabria) | CATANIA MRE = 5.92% CALABRIA MRE = 7.72% MILANO MRE = 8.03% |
DOUBLEDAY et al. [146] | 2021 | Bayesian Model Averaging | Texas | 1, 4, 12, and 24 h ahead | Two-plus years of data November 2016 to December, 2018. | CRPS score of 5.18 to 7.47 varies from site to site. |
KHODAYAR et al. [147] | 2020 | Convolutional Graph Auto encoder | USA | 30-min up to 6 h ahead GHI | 1998 up to 2016 | CGAE obtains 2.53% better CRPS than ST-QR-Lasso |
Reference | Year | Model | Location | Forecast Horizon | Data | Conclusion |
---|---|---|---|---|---|---|
SANJARI et al. [99] | 2017 | Markov Chain, Gaussian mixture and Genetic algorithm | Australia | 15-min ahead | Two year (2014 and 2015) | CRPS 2.16 |
Yona et al. [138] | 2013 | Fuzzy theory, RNN | Okinawa, Japan | 24 h head | 1 year of data | Average MAE of 0.1327 |
Voyant et al. [53] | 2012 | ANN and ARMA | Mediterranean, France | 1 h ahead | 6 years data | average nRMSE is 14.9% |
Marzouq et al. [54] | 2013 | GA-MLP | Fez in Morocco | Daily | 7 years (2009 to 2015 | = 0.975 |
Perveen et al. [149] | 2019 | ANFIS | India | 10 min ahead | 15 years (2002 to 2016) | Average MAPE = 0.00000021% |
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Yeom et al. [151] | 2020 | CNN- LSTM network | Korean Peninsula. | 1 h ahead | 1 April 2011 to 31 December 2015 | RMSE value of 71.334 W/m2 and value of 0.895. |
D. Yang et al. [152] | 2021 | AnEn+LPQR | Oahu Solar Measurement Grid, Hawaii. | 4 s to 1 min ahead | 2010 March to 2011 October | CRPS score of 24.7 to 64.5 and Average skill score is 27.80% |
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M. Ghayekhloo [156] | 2015 | Game Theory (GT)-SOM | Ames, Iowa, United States | 1 h, 2 h, 3 h and 1 day ahead | 2011 and 2013 | RMSE value of 67.921, 82.506, 113.4 and 119.75 W/m2 respectively |
Monjoly et. Al [79] | 2017 | WD–AR | Le Raizet, France | 1 h | January 2012 to December 2013 | RMSE value of 19.57% |
Monjoly et. Al [79] | 2017 | WD–AR–ANN | Le Raizet, France | 1 h | January 2012 to December 2013 | RMSE value of 7.90% |
Reference | Year | Model | Location | Forecast Horizon | Data | Training/Test Split Ratio | Conclusion |
---|---|---|---|---|---|---|---|
Yongqi Liu et al. [5] | 2019 | CNN and GRU | United States | 3 h-ahead GHI | 2 years (1 January 2013 to 31 December 2014) | 8760 h / 8760 h | Mean RMSE of 69.5 W/m2 |
Davide Cannizzaro et al. [157] | 2021 | Convolutional Neural Networks (CNN) and Random Forest (RF) | University Campus in Turin, Italy, | Next 15 min up to next 24 h GHI | December 2009 to November 2015 | (6 years) December 2009 to November 2014/December 2014 to November 2015 | coefficient of 0.936 to 0.908 |
Davide Cannizzaro et al. [157] | 2021 | Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) | University Campus in Turin, Italy | Next 15 min up to next 24 h GHI | December 2009 to November 2015 with a time- resolution of 15 min (6 years) | December 2009 to November 2014/December 2014 to November 2015 | coefficient of 0.937 to 0.908 |
Pratima Kumari et al. [158] | 2020 | Extreme gradient boosting forest and Deep neural networks (XGBF-DNN) | New Delhi, Jaipur and Gangtok in India | 1 h GHI ahead | Ten years (from 2005 to 2014) | First eight years of data/Two years of data. | RMSE of 56.68 W/m2, 53.78 W/m2 and 91.86 W/m2 of Jaipur, New Delhi, and Gangtok respectively. |
Nonita Sharma et al. [159] | 2021 | Long Short Term Memory (LSTM) Layer and Maximal Overlap Discrete Wavelet Transform (MODWT) | Yulara Solar System, Australia | 1 day, 10 days, and 1 month ahead GHI | January 2016 (12:00:00 a.m.) to 10 June 2020 (4:50:00 a.m.) | 2016–2019/2020 | RMSE of 0.1109, 0.1231, and 0.1231 kW for 1 day, 10 days, and 1 month, respectively |
Fermín Rodríguez et al. [160] | 2021 | Feed forward neural network and a Spatio-temporal approach | Vitoria–Gasteiz, Spain | 10 min ahead GHI | 2015–2017 (3 years) | 2015–2016/2017 | RMSE of 50.80 W/m2 |
Waqas Khan et al. [161] | 2021 | DSE-XG (ANN, LSTM and XGBoost) | Bunnik, Netherlands | 15 min and 1 h ahead GHI | 2016 to 2019 years data by solar gis | Four folds/One fold | RMSE of 0.35, and 0.26 kW for 15 min. and 1 h respectively |
Liping Liu et al. [104] | 2019 | SVM, MLP and MARS | Australia Solar Centre (DKASC), Australia | 1 day ahead GHI | 15 August 2013 up to 17 June 2018 | 4 months of each year (from 2014 to 2018), with a total of 600/4 days in 2018 | RMSE of 0.1248 to 0.53 kW |
Horizon Mostly Used—Model | Source—Model |
---|---|
Very short term—Blue—1,4,6,8 | Geographical & Meteorological data—Blue—1,3,8 |
Short term—Brown—2,3,4,5,6,7,8 | Cloud & Satellite Imagery data—Brown—2,8 |
Medium term—Black—3,4,6,8 | NWP data—Black—2,3,4,6,7,8 |
Long term—Green—2,3,8 | Historical data—Green—3,4,5,6,7,8 |
Error evaluation—Violet | Real time monitoring data—2,8 |
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Sudharshan, K.; Naveen, C.; Vishnuram, P.; Krishna Rao Kasagani, D.V.S.; Nastasi, B. Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction. Energies 2022, 15, 6267. https://doi.org/10.3390/en15176267
Sudharshan K, Naveen C, Vishnuram P, Krishna Rao Kasagani DVS, Nastasi B. Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction. Energies. 2022; 15(17):6267. https://doi.org/10.3390/en15176267
Chicago/Turabian StyleSudharshan, Konduru, C. Naveen, Pradeep Vishnuram, Damodhara Venkata Siva Krishna Rao Kasagani, and Benedetto Nastasi. 2022. "Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction" Energies 15, no. 17: 6267. https://doi.org/10.3390/en15176267