Performance Evaluation of Various Solar Forecasting Models for Structural & Endogenous Datasets
DOI:
https://doi.org/10.13052/dgaej2156-3306.3825Keywords:
Solar forecasting, learning rate, support vector machine, artificial neural network, moving windowAbstract
The forecasting of solar irradiation with high precision is critical for fulfilling electricity demand. The dataset used to train the learning-based models has a direct impact on the model’s prediction accuracy. This work evaluates the impact of two types of datasets: structural and endogenous datasets over the prediction accuracy of different solar forecasting models (five variants of artificial neural network (ANN) based models, Support vector machine (SVM), Linear Regression, Bagged and Boosted Regression tree). The issue of variability estimation is also explored in the paper in order to choose the best model for a given dataset. The performance of the models is assessed using two essential error metrics: mean absolute percentage error (MAPE) and root mean square error (RMSE). The results shows that the MAPE and RMSE for structural data vary from 1.99% to 29.73% and 23.39 W/m2 to 165.21 W/m2, respectively, whereas these errors for endogenous dataset ranges from 1.98% to 31.19% and 23.64 W/m22 to 152.56 W/m22. Moreover, these findings, together with the data variability findings, suggest that SVM is the best model for all forms of data variability, whereas CFNN may be employed for greater variability.
Downloads
References
P. Singla, M. Duhan, and S. Saroha, “A comprehensive review and analysis of solar forecasting techniques,” Front. Energy, pp. 1–37, Mar. 2021, doi: 10.1007/s11708-021-0722-7.
P. Singla, M. Duhan, and S. Saroha, “Solar Irradiation Forecasting by Long-Short Term Memory Using Different Training Algorithms,” pp. 81–89, 2022, doi: 10.1007/978-981-16-4663-8_7.
S. Sobri, S. Koohi-Kamali, and N. A. Rahim, “Solar photovoltaic generation forecasting methods: A review,” Energy Conversion and Management, vol. 156. Elsevier Ltd, pp. 459–497, Jan. 15, 2018. doi: 10.1016/j.enconman.2017.11.019.
P. Singla, M. Duhan, and S. Saroha, “An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network,” Earth Sci. Informatics, vol. 15, no. 1, pp. 291–306, Nov. 2021, doi: 10.1007/S12145-021-00723-1/TABLES/7.
G. Notton, C. Voyant, A. Fouilloy, J. L. Duchaud, and M. L. Nivet, “Some applications of ANN to solar radiation estimation and forecasting for energy applications,” Appl. Sci., vol. 9, no. 1, pp. 1–21, 2019, doi: 10.3390/app9010209.
D. O’Leary and J. Kubby, “Feature Selection and ANN Solar Power Prediction,” J. Renew. Energy, vol. 2017, pp. 1–7, Nov. 2017, doi: 10.1155/2017/2437387.
R. Meenal, A. Immanuel Selvakumar, S. Berclin JeyaPrabha, and E. Rajasekaran, “Solar Mapping of India using Support Vector Machine,” J. Phys. Conf. Ser., vol. 1142, p. 012010, Nov. 2018, doi: 10.1088/1742-6596/1142/1/012010.
A. Zendehboudi, M. A. Baseer, and R. Saidur, “Application of support vector machine models for forecasting solar and wind energy resources: A review,” Journal of Cleaner Production, vol. 199. Elsevier Ltd, pp. 272–285, Oct. 20, 2018. doi: 10.1016/j.jclepro.2018.07.164.
W. Sharika, L. Fernando, A. Kanagasundaram, R. Valluvan, and A. Kaneswaran, “Long-term Solar Irradiance Forecasting Approaches – A Comparative Study,” Dec. 2018. doi: 10.1109/ICIAFS.2018.8913381.
C. G. Ozoegwu, “Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number,” J. Clean. Prod., vol. 216, pp. 1–13, Apr. 2019, doi: 10.1016/J.JCLEPRO.2019.01.096.
W. I. Hameed et al., “Prediction of Solar Irradiance Based on Artificial Neural Networks,” Inventions, vol. 4, no. 3, p. 45, Aug. 2019, doi: 10.3390/inventions4030045.
S. Amely Jumaat, F. Crocker, M. Helmy Abd Wahab, N. Hanis Mohammad Radzi, and M. Fakri Othman, “Prediction of Photovoltaic (PV) Output Using Artificial Neutral Network (ANN) Based on Ambient Factors,” J. Phys. Conf. Ser., vol. 1049, p. 012088, Jul. 2018, doi: 10.1088/1742-6596/1049/1/012088.
S. Al-Dahidi, O. Ayadi, J. Adeeb, and M. Louzazni, “Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction,” Front. Energy Res., vol. 7, p. 130, Nov. 2019, doi: 10.3389/fenrg.2019.00130.
S. Rahman, S. Rahman, and A. K. M. Bahalul Haque, “Prediction of Solar Radiation Using Artificial Neural Network,” J. Phys. Conf. Ser., vol. 1767, no. 1, p. 012041, Feb. 2021, doi: 10.1088/1742-6596/1767/1/012041.
L. Huang, J. Kang, M. Wan, L. Fang, C. Zhang, and Z. Zeng, “Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events,” Front. Earth Sci., vol. 9, p. 202, Apr. 2021, doi: 10.3389/FEART.2021.596860/BIBTEX.
M. Guermoui, K. Gairaa, J. Boland, and T. Arrif, “A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study,” J. Sol. Energy Eng., vol. 143, no. 2, Apr. 2021, doi: 10.1115/1.4047852.
A. El Mouatasim and Y. Darmane, “Regression analysis of a photovoltaic (PV) system in FPO,” in AIP Conference Proceedings, Dec. 2018, vol. 2056, no. 1, p. 020008. doi: 10.1063/1.5084981.
P. Singla, M. Duhan, and S. Saroha, “A Hybrid Solar Irradiance Forecasting Using Full Wavelet Packet Decomposition and Bi-Directional Long Short-Term Memory (BiLSTM),” Arab. J. Sci. Eng., pp. 1–27, Mar. 2022, doi: 10.1007/S13369-022-06655-2/TABLES/18.
P. Singla, M. Duhan, and S. Saroha, “A dual decomposition with error correction strategy based improved hybrid deep learning model to forecast solar irradiance,” https://doi.org/10.1080/15567036.2022.2056267, vol. 44, no. 1, pp. 1583–1607, Mar. 2022, doi: 10.1080/15567036.2022.2056267.
“Photovoltaic Power Systems Technology Collaboration Program Snapshot of Global PV Markets: Report IEA PVPS T1-35:2019,” 2019.
“Ahmedabad climate: Average Temperature, weather by month, Ahmedabad weather averages – Climate-Data.org.” https://en.climate-data.org/asia/india/gujarat/ahmedabad-2828/ (accessed Apr. 24, 2020).
C. Yousif, G. O. Quecedo, and J. B. Santos, “Comparison of solar radiation in Marsaxlokk, Malta and Valladolid, Spain,” Renew. Energy, vol. 49, pp. 203–206, 2013, doi: 10.1016/j.renene.2012.01.031.
J. Zeng and W. Qiao, “Short-term solar power prediction using a support vector machine,” Renew. Energy, vol. 52, pp. 118–127, Apr. 2013, doi: 10.1016/j.renene.2012.10.009.
C. Huang, L. Wang, and L. L. Lai, “Data-Driven Short-Term Solar Irradiance Forecasting Based on Information of Neighboring Sites,” IEEE Trans. Ind. Electron., vol. 66, no. 12, pp. 9918–9927, Dec. 2019, doi: 10.1109/TIE.2018.2856199.
S. Y. Heng et al., “Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction,” Sci. Reports 2022 121, vol. 12, no. 1, pp. 1–18, Jun. 2022, doi: 10.1038/s41598-022-13532-3.
M. Sridharan, “Application of Generalized Regression Neural Network in Predicting the Performance of Natural Convection Solar Dryer,” J. Sol. Energy Eng. Trans. ASME, vol. 142, no. 3, Jun. 2020, doi: 10.1115/1.4045384/1066328.
G. Dudek, “Generalized regression neural network for forecasting time series with multiple seasonal cycles,” Adv. Intell. Syst. Comput., vol. 323, pp. 839–846, 2015, doi: 10.1007/978-3-319-11310-4_73.
Z. Boussaada, O. Curea, A. Remaci, H. Camblong, and N. M. Bellaaj, “A nonlinear autoregressive exogenous (NARX) neural network model for the prediction of the daily direct solar radiation,” Energies, vol. 11, no. 3, 2018, doi: 10.3390/en11030620.
I. Majumder, R. Bisoi, N. Nayak, and N. Hannoon, “Solar power forecasting using robust kernel extreme learning machine and decomposition methods,” Int. J. Power Energy Convers., vol. 11, no. 3, pp. 260–290, 2020, doi: 10.1504/IJPEC.2020.107958.
“Cascade-forward neural network - MATLAB cascadeforwardnet.” https://www.mathworks.com/help/deeplearning/ref/cascadeforwardnet.html;jsessionid=b7bd9600b0fddad3c28a11df0cd7 (accessed Apr. 24, 2020).
S. Shamshirband et al., “Estimating the diffuse solar radiation using a coupled support vector machine-wavelet transform model,” Renewable and Sustainable Energy Reviews, vol. 56. Elsevier Ltd, pp. 428–435, Apr. 01, 2016. doi: 10.1016/j.rser.2015.11.055.
S. Ibrahim, I. Daut, Y. M. Irwan, M. Irwanto, N. Gomesh, and Z. Farhana, “Linear regression model in estimating solar radiation in perlis,” Energy Procedia, vol. 18, no. September 2015, pp. 1402–1412, 2012, doi: 10.1016/j.egypro.2012.05.156.
R. Ahmed, V. Sreeram, Y. Mishra, and M. D. Arif, “A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization,” Renew. Sustain. Energy Rev., vol. 124, no. June 2019, p. 109792, 2020, doi: 10.1016/j.rser.2020.109792.
P. Singla, M. Duhan, and S. Saroha, “Review of Different Error Metrics: A Case of Solar Forecasting,” AIUB J. Sci. Eng., vol. 20, no. 4, pp. 158–165, Dec. 2021, doi: 10.53799/AJSE.V20I4.212.
S. Sayago, G. Ovando, J. Almorox, and M. Bocco, “Daily solar radiation from NASA-POWER product: assessing its accuracy considering atmospheric transparency,” Int. J. Remote Sens., vol. 41, no. 3, pp. 897–910, Feb. 2020, doi: 10.1080/01431161.2019.1650986.