A New Hybrid Short Term Solar Irradiation Forecasting Method Based on CEEMDAN Decomposition Approach and BiLSTM Deep Learning Network with Grid Search Algorithm
DOI:
https://doi.org/10.13052/dgaej2156-3306.3842Keywords:
Deep learning network, complete ensemble EMD with adaptive noise, gate recurrent unit, long short term memory, bidirectional long short term memory, Diebold Mariano Hypothesis test, directional change in forecasting, hyper parametersAbstract
An accurate and efficient forecasting of solar energy is necessary for managing the electricity generation and distribution in today’s electricity supply system. However, due to its random character in its time series, accurate forecasting of solar irradiation is a difficult task; but it is important for grid management, scheduling and its balancing. To fully utilize the solar energy in order to balance the generation and consumption, this paper proposed an ensemble approach using CEEMDAN-BiLSTM combination to forecast short term solar irradiation. In this, Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) extract the inherent characteristics of time series data by decomposing it into low and high frequency Intrinsic Mode Functions (IMF’s) and Bidirectional Long Short Term Memory (BiLSTM) used as a forecasting tool to forecast the solar Global Horizontal Irradiance (GHI). Furthermore, using extensive experimental analysis, the research minimizes the number of IMF’s by integrating the CEEMDAN decomposed component (IMF1–IMF14) in order to increase the prediction accuracy. Then, for each IMF subseries, the trained standalone BiLSTM network are assigned to carry out the forecasting. In last stage, the forecasted results of each BiLSTM network are aggregate to compile final results. Two year data (2012–13) of Delhi, India from National Solar Radiation Database (NSRDB) has been used for training while one year data (2014) used for testing purpose for the same location. The proposed model performance is measured in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), Correlation coefficient (R22) and forecast skill (FS). For the comparative analysis of proposed model, several others models: persistence model, unidirectional deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), BiLSTM and two CEEMDAN based BiLSTM models are developed. The proposed model achieved lowest annual average RMSE (18.86 W/m22, 22.24 W/m22, 26.25 W/m22) and MAPE (2.19%, 4.81%, 6.77%) among the other developed models for 1-hr, 2-hr and 3-hr ahead solar GHI forecasting respectively. The maximum correlation coefficient (R22) obtained by the proposed model is 96.4 for 1-hr ahead respectively; on the other hand, forecast skill (%) of 89% with reference to benchmark model. Various test such as: Diebold Mariano Hypothesis test (DMH) and directional change in forecasting (DC) are used to analyze the sensitivity with reference to the difference in forecasted and observed value.
Downloads
References
A. Gupta, K. Gupta and S. Saroha, A review and evaluation of solar forecasting technologies Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2021.04.491
Gupta, Anuj.; Gupta Kapil.; Saroha, Sumit.; Solar Irradiation Forecasting Technologies: A Review: Strategtic planning for Energy and the Environemnt.2020: Vol 30, Iss 3–4, 2020. https://doi.org/10.13052/spee1048-4236.391413
M. Kocifaj, Sky luminance/radiance model with multiple scattering effect, Sol. Energy. 83 (2009) 1914–1922. https://doi.org/10.1016/j.solener.2009.07.004.
M. Kocifaj, M. Gangl, F. Kundracik, H. Horvath, G. Videen, Simulation of the optical properties of single composite aerosols, J. Aerosol Sci. 37 (2006) 1683–1695. https://doi.org/10.1016/j.jaerosci.2006.08.002.
Liang, Li.; Zhi Li.; Haiwei, Yu.; Medium load forecasting method with improved deep belief network for renewable. Distributed General and alternative energy journal. 2022: Vol 37, Iss 3, 2022. https://doi.org/10.13052/10.13052/dgaej2156-3306.3735
M. Kocifaj, Angular distribution of scattered radiation under broken cloud arrays: An approximation of successive orders of scattering. Sol. Energy. 86 (2012) 3575–3586. https://doi.org/10.1016/j.solener.2012.06.022.
Long, Fei.; Liu Fei.; Peng, Xiangli.; Yu, Zheng.; Power quality disturbance identification and optimization based on machine learning. Distributed General and alternative energy journal. 2022: Vol 37, Iss 2, 2022. https://doi.org/10.13052/dgaej2156-3306.3723
M. Q. Raza, M. Nadarajah, C. Ekanayake, On recent advances in PV output power forecast, Sol. Energy. 136 (2016) 125–144. https://doi.org/10.1016/j.solener.2016.06.073
C. Voyant, G. Notton, S, Kalogirou, M. L. Nivet, C. Paoli, F. Motte, A. Fouiloy, Machine learning methods for solar radiation forcasting: A review, Renew. Energy.
U. K. Das, K. S. Tey, M. Seyedmahmoudian, S. Mekhilef, M. Y. I. Idris, W. VanDeventrer, B. Horan, A. Stojcevski, Forecasting of photovoltaic power generation and model optimization: A Review, Renew. Sustain. Energy Rev. 81 (2018) 912–928. https://doi.org/10.1016/j.rser.2017.08.017
R. Perez. S. Kivalov, J. Schlemmer, K. Hemker, D. Renne, T. E. Hoff, Validation of short and medium term operational solar radiation forecastes in the US, Sol. Energy. 84 (2010) 2161–2172. https://doi.org/10/1016/j.solener.2010.08.014.
H. Yang, J. Kleissl. Preprocessing WRF initial conditions of coastal stratocumulus forecasting, Sol. Energy. 133 (2016) 180–193. https://doi.org/10/1016/j.solener.2016.04.003.
R. Perez, E. Lorenz, S. Pelland, Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe, Sol. Energy. 94 (2013) 305–326. https://doi.org/10.1016/j.solener.2013.05.005.
X. Mi, H. Liu, Y. Li, Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine, Energy Convers. Manag. 180 (2019) 196–205. https://doi.org/10.1016/j.enconman.2018.11.006.
M. Kocifaj, L. Komar, Modeling diffuse irradiance under arbitrary and homogenous skies: Comparison and validation, Appl. Energy. 166 (2016) 117–127. https://doi.org/10.1016/j.apenergy.2016.01.024.
M. Kocifaj, Unified model of radiance patterns under arbitrary sky conditions, Sol. Energy. 115 (2015) 40–51. https://doi.org/10/1016/j.solener.2015.2015.02.019.
G. Wnag, Y. Su. L. Shu, One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models, Renew. Energy. 96 (2016) 469–478. https://doi.org/10.1016/j.renene.2016.04.089.
D. Yang, P. Jirutitijaroen, W. M. Walsh, Hourly solar irradiance time series forecasting using cloud cover index, Sol. Energy. 86 (2012) 3531–3543. https://doi.org/10.1016/j.solener.2012.07.029
X. Huang, J. Shi, B. Gao, Y. Taj, Z. Chen, J. Chen, J. Zhang, Forecasting Hourly Solar Irradiance Using Hybrid wavelet transformation and elman model in smart grid, IEEE access. 7 (2019) 139909–139923. https://doi.org/10.1109/Access.2019.2943886.
C. C. Turrado, M.delC. M. Lopez, F. S. Lasheras, B. A. R. Gomez, J. L. C. Rolle, F. J.deC. Juez, Missing data imputation of solar radiation data under different atmospheric conditions, Sensors (Switzerland). 14 (2014) 20382–20399. https://doi.org/10.3390/s141120382.
R. C. Deo, X. Wen, F. Qi, A.wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset, Appl. Energy. 168 (2016) 568–593. https://doi.org/10.1016/j.apenergy.2016.01.130.
F. Baser, H. Demirhan, A.fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation, Energy. 123 (2017) 229–240. https://doi.org/10.1016/j.energy.2017.02.008.
Z. Dong, D. Yang, T. Reindl, W. N. Walsh, A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance, Energy. 82 (2015) 507–577. https://doi.org/10.1016/j.energy.2015.01.066.
B. Amrouche, X. LePivert, Artificial neural network based daily local forecasting for global solar radiation, Appl. Energy. 130 (2014) 333–341. https://doi.org/10.1016/j.apenergy.2014.05.055.
H. Bouzgou, C. A. Gueymard, Fast short-term global solar irradiance forecasting with wrapper mutual information, Renew. Energy. 133 (2019) 1055–1065. https://doi.org/10.1016/j.renene.2018.10.096.
H. Liu, H. Q. Tian, X. F. Liang, Y. F. Li, Wind speed forecasting approach using secondary decomposition algorithms and Elman neural network, Appl. Energy. 157–735 (2015) 183–194. https://doi.org/10.1016/j.apenergy.2015.08.014
S. Monjoly, M. Andre, R. Calif, T. Soubdhan, Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach, Energy. 119 (2017) 288–298. https://doi.org/10.1016/j.energy.2016.11.061.
S. Sun, S. Wang, G. Zhang, J. Zheng, A decomposition-clustering-ensemble learning approach for solar radiation forecasting, Sol. Energy. 163 (2018) 189–199. https://doi.org/10.1016/j.solener.2018.02.006.
H. Lan, H. Yin, Y. Y. Hong, S. Wen, D. C. Yu, P. Cheng, Day-ahead spatiotemporal forecasting of solar Irradiation along a navigation route, Appl. Energy, 211 (2018), 15–27 https://doi.org/10.1016/j.apenergy.2017.11.014.
K. Mohammadi, S. Shamshirband, C. W. Tong, M. Arif, D. Petkovic. A new hybrid support vector machine-wavelet transform approach for estimation of horizontal global solar radiation, Energy Convers. Manag. 92 (2015) 162–171. https://doi.org/10.1016/j.enconman.2014.12.050.
S. Hussain, A, Alalili, A hybrid soalr radiation modeling approach using wavelet multiresolution analysis and artificial neural networks, Appl. Energy. (2017). https://doi.org/10.1016/j.apenergy.2017.09.100.
Qing X, Niu Y, Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148: 461–468 (2018). https://doi.org/10.1016/j.energy.2018.01.177.
Kumari P, Toshniwal D, Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance. Clean Prod 279:123285 (2021) https://doi.org/10.1016/j.jclepro.2020.123285.
Zang H, Cheng L, Ding T, Cheung KW, Wei Z, Sun G, Day-ahead photovoltaic power forecasting approach based on deep convolution neural networks and meta learning. Int J. Electr Power Energy Syst 118:105790 (2020a), https://doi.org/10.1016/j.ijepes.2019.105790.
Feng C, Zhang J, SolarNet: a sky image-based deep convolution neural network for intra-hour solar forecasting. Sol Energy 204: 71–78 (2020). https://doi.org/10.1016/j.solener.2020.03.083
Liu D, Sun K, Random forest solar power forecast based on classification optimization. Energy 187 (2019). https://doi.org/10.1016/j.energy.2019.115940.
Mishra M. Byomakesha Dash P, Nayak J, Naik B, Kumar Swain S, Deep learning and wavelet transform integrated approach for short term solar PV power prediction. Mesa J Int Meas Confed 166:108250. https://doi.org/10.1016/j.measurement.2020.108250.
Gao. B, Huang X, Shi J, Tai Y, Xiao R, Prediction day-ahead solar irradiance through gate recurrent unit using weather forecasting data. J. Renew Sustain Energy 11(4):043705 (2019). https://doi.org/10.1063/1.5110223.
Ding M, Zhou H, Xie H, Wu M, Nakanishi Y, Yokoyama R, A gated recurrent unit neural networks based wind speed error correction model for short term wind power forecasting. Neurpcomputing 365: 54–61 (2019). https://doi.org/10.1016/j.neucom.2019.07.058
Kumar D, Mathur HD, Bhanot S, Bansal RC, Forecasting of solar and wind power using LSTM RNN for load frequency control in isolated microgrid. Int Journal of Model Simulink (2020). https://doi.org/10.1080/0228203.2020.1767840
Hu YL, Chen L, A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and differential evolutional algorithms. Energy Convers Manag. 173:123–142. https://doi.org/10.1016/j.enconman.2018.07.070.
Li C, Zhang Y, Zhao G, Ren Y, Hourly solar irradiance prediction using deep BiLSTM network. Earth Sci Informatics 14:299–309. https://doi.org/10.1007/s12145-020-005113
Rai A, Shrivastava A, Jana KC, A CNN-BiLSTM based deep learning model for mid-term solar radiation prediction. Int Trans Electr Energy Syst 11(18):8613 (2021). https;//doi.org/10.3390/app11188613.
Wu, Z.; Huang, N. E. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv. Adapt. Data Anal. 2009, 1, 1–41.
K. M. Chang, Ensemble empirical model decomposition: A Noise assisted, Biomed. Tech.55 (2010) 193–201. https://doi.org/10.1515/BMT.2010.030.
P. Flandrin, E. Torres, M. A. Colominas, A complete ensemble empirical model decomposition with adaptive noise, in:2011 IEEE Int. Conf. Acoust. Speech Signal Process., IEEE, Prague, 2011:pp.4144–4147.
Zang H, Liu L, Sun L, Cheng L, Wei Z, Sun G, short term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations. Renew Energy 160:26–41 (2020b). https://doi.org/10.1016/j.renene.2020.05.150.
Hochreiter S, Schmidhuber J (1997) Long short term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Kulshrestha A, Krishnaswamy V, Sharma M (2020) Bayesian BiLSTM approach for tourism demand forecasting. AnnTour Res 83:102925 (2020). https://doi.org/10.1016/j.annals.2020.102925.
Fischer T, Krauss C, Deep learning with long short term memory networks for financial market prediction. Eur J Oper Res 270(2):654–669 (2018). https://doi.org/10.1016/j.ejor.2017.11.054.
Soreng, bineeta.; Zhi Li.; Haiwei, Yu.; An optimal islanding detection scheme for an inverter based distributed generation system. Distributed General and alternative energy journal. 2022: Vol 36, Iss 2, 2021. https://doi.org/10.13052/10.13052/dgaej2156-3306.3735
Yildirim O, A novel wavelet sequences based on deep bidirectional LSTM network model for EEG signal classification. Comput Biol Med 96:189–202 (2018). https://doi.org/10.1016/j.compbiomed.2018.03.016
Bedi J, Toshniwal D, Deep learning framework to forecast electricity demand. Appl Energy 238:1312–1326 (2019). https;//doi.org/10.1016/j.renene.2018.08.044
Yousif C, Quecedo GO, Santos JB, Comparison of solar radiation in Marsaxlokk, Malta and Valladolid, Spain. Renew Energy 49:203–206 (2013). https://doi.org/10.1016/j.renene.2012.01.031
Benali L, Notton G, Fouilloy A, Voyant C, Dizene R, Solar radiation forecasting using artificial neural network and random forest methods: application tonormal beam, horizontal diffuse and global components. Renew Energy 132:871–884 (2019). https://doi.org/10.1016/j.renene.2018.08.044.
http://delhitourism.gov.in/delhitourism/aboutus/seasons_of_delhi.jsp
Gupta A., Gupta K., Saroha S, Solar Energy Radiation Forecasting Method. In: Agarwal P., Mittal M., Ahmed J., ldrees S. M. (eds) Smart Technologoies for Energy and Environmental Sustainability. Green Energy and Technology. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-80702-3_7
Singla, P., Duhan, M. & Saroha, S. An ensemble method to forecast 24-h ahead solar irradiance using Wavelet decomposition and BiLSTM deep learning network, Earth Sci Inform 15, 291–306 (2022). https://doi.org/10.1007/s12145-021-00723-1
Fan, J.; Wu, L.; Ma, X.; Zhou, H.; Zhang, F.: Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air polluted regions. Renew. Energy 145, 2034–2045 (2020). https://doi.org/10.1016/j.renene.2019.07.104
Gupta, Anuj.; Gupta, Kapil.; Saroha, Sumit.; Short term solar irradiation prediction framework based on EEMD-GA-LSTM Method: Strategic Planning for Energy and the Environment, Vol. 41_3, 255–280. https://doi.org/10.13052/spee1048-5236.4132
Gupta, A., Gupta, K., & Saroha, S. (2022). Short Term Solar Irradiation Forecasting using CEEMDAN Decomposition Based BiLSTM Model Optimized by Genetic Algorithm Approach. International Journal of Renewable Energy Development, 11(3), 736–750. https://doi.org/10.14710/ijred.2022.45314
Gupta, A., Gupta, K., Saroha, S. (2023). Single-Step Ahead Solar Irradiance Forecasting Using Hybrid WT-PSO-Based Neural Network. In: Namrata, K., Priyadarshi, N., Bansal, R. C., Kumar, J. (eds) Smart Energy and Advancement in Power Technologies. Lecture Notes in Electrical Engineering, vol 927. Springer, Singapore. https://doi.org/10.1007/978-981-19-4975-3_31.
