Short Term Solar Irradiation Prediction Framework Based on EEMD-GA-LSTM Method
Accurate short term solar irradiation forecasting is necessary for smart grid stability and to manage bilateral contract negotiations between suppliers and customers. Traditional machine learning methods are unable to acquire and rectify nonlinear characteristics from solar dataset, which not only complicates model construction but also affect prediction accuracy. To address these issues, a deep learning based architecture with predictive analysis strategy is developed in this manuscript. In the first stage, the original solar irradiation sequences are divided into many intrinsic mode functions to generate a prospective feature set using a sophisticated signal decomposition technique. After that, an iteration method is used to generate a prospective range of frequency related to deep learning model. This method is created by linked algorithm using the GA and deep learning network. The findings by the proposed model employing sequences obtained by the preprocessing methodology considerable improve prediction accuracy as comparison to conventional models. In contrast, when confronted with a high resolution dataset derived from big data set, the chosen dataset may not only conduct a huge data reduction, but also enhances forecasting accuracy up to 22.74 percent over a variety of evaluation metrics. As a result, the proposed method might be used to predict short-term solar irradiation with greater accuracy using a solar dataset.
Vasylieva, T.; Lyulyov, O.; Bilan, Y.; Streimikiene, D. Sustainable economic development and greenhouse gas emissions: The dynamic impact of renewable energy consumption, GDP, and corruption. Energies 2019, 12, 3289.
Gupta, Anuj.; Gupta, Kapil.; Saroha, Sumit.; Solar Irradiation Forecasting Technologies: A Review: Strategic Planning for Energy and the Environemnt.2020: Vol. 39 Iss. 3–4 2020. https://doi.org/10.13052/spee1048-4236.391413
Gupta, Anuj.; Gupta, Kapil.; Saroha Sumit.; A Review and Evaluation of Solar Forecasting Technologies: Materials today proceedings 2021, Volume 47, Part 10, 2021, Pages 2420–2425. https://doi.org/10.1016/j.matpr.2021.04.491
Al-Hajj, R.; Assi, A.; Fouad, M.M. Forecasting Solar Radiation Strength Using Machine Learning Ensemble. In Proceedings of the 7th IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Paris, France, 14–17 October 2018; pp. 184–188.
Gupta A., Gupta K., Saroha S. (2022) Solar Energy Radiation Forecasting Method. In: Agarwal P., Mittal M., Ahmed J., Idrees S.M. (eds) Smart Technologies for Energy and Environmental Sustainability. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-80702-3_7
Singla P, Duhan M, Saroha S (2021) A comprehensive review and analysis of solar forecasting techniques. Front Energy. https://doi.org/10.1007/s11708-021-0722-7
Olatomiwa, L.; Mekhilef, S.; Shamshirband, S.; Mohammadi, K.; Petković, D.; Sudheer, C. A support vector machine–firefly algorithm-based model for global solar radiation prediction. Sol. Energy 2015, 115, 632–644.
Fan, J.; Wu, L.; Zhang, F.; Cai, H.; Zeng, W.; Wang, X.; Zou, H. Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China. Renew. Sustain. Energy Rev. 2019, 100, 186—212.
Gupta A., Gupta K., Saroha S. (2022) Single Step-Ahead Solar Irradiation Forecasting Based on Empirical Mode Decomposition with Back Propagation Neural Network. In: Gupta O.H., Sood V.K., Malik O.P. (eds) Recent Advances in Power Systems. Lecture Notes in Electrical Engineering, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-16-6970-5_10
Al-Hajj, R.; Assi, A.; Fouad, M. Short-Term Prediction of Global Solar Radiation Energy Using Weather Data and Machine Learning Ensembles: A Comparative Study. J. Sol. Energy Eng. 2021, 8, 1–38.
Richardson DS, Cloke HL, Pappenberger F (2020) Evaluation of the consistency of ECMWF ensemble forecasts. Geophys Res Lett 47(11). https://doi.org/10.1029/2020GL087934
Perez R, Kivalov S, Schlemmer J, Hemker K, Hoff TE (2012) Shortterm irradiance variability: Preliminary estimation of station pair correlation as a function of distance. Solar Energy 86(8) Pergamon:2170–2176. https://doi.org/10.1016/j.solener.2012.02.027
Piri, J.; Shamshirband, S.; Petković, D.; Tong, C.W.; Rehman, M.H. Prediction of the solar radiation on the earth using support vector regression technique. Infrared Phys. Technol. 2015, 68, 179–185.
Shadab A, Ahmad S, Said S (2020) Spatial forecasting of solar radiation using ARIMA model. Remote Sens Appl Soc Environ 20:100427. https://doi.org/10.1016/j.rsase.2020.100427
Jahani B, Mohammadi B (2019) A comparison between the application of empirical and ANN methods for estimation of daily global radiation in Iran. Theor Appl Climatol 137(1–2):1257–1269. https://doi.org/10.1007/s00704-018-2666-3
Dumitru C-D, GligorA, Enachescu C 9(2016) Solar photovoltaic energy production forecast using neural networks. Procedia Technol 22: 808–815. https://doi.org/10.1016/j.protcy.2016.01.053
Zeng J, Qiao W (2013) Short-term solar power prediction using a support vector machine. Renew Energy 52:118–127. https://doi.org/10.1016/j.renene.2012.10.009
Gupta A., Gupta K., Saroha S. (2022) A Comparative Analysis of Neural Network-Based Models for Forecasting of Solar Irradiation with Different Learning Algorithms. In: Khosla A., Aggarwal M. (eds) Smart Structures in Energy Infrastructure. Studies in Infrastructure and Control. Springer, Singapore. https://doi.org/10.1007/978-981-16-4744-4_2
Monjoly, Stéphanie; André, Maïna; Calif, Rudy; Soubdhan, Ted (2017). Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach. Energy, 119(), 288–298. https://doi:10.1016/j.energy.2016.11.061
Zendehboudi, Alireza; Baseer, M.A.; Saidur, R. (2018). Application of support vector machine models for forecasting solar and wind energy resources: A review. Journal of Cleaner Production, 199, 272–285. https://doi:10.1016/j.jclepro.2018.07.164
Chen, C.-R.; Ouedraogo, F.B.; Chang, Y.-M.; Larasati, D.A.; Tan, S.-W. Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS. Mathematics 2021, 9, 2438. https://doi.org/10.3390/math9192438
Qing X, Niu Y (2018) hourly day ahead solar irradiance predictions using weather forecasts by LSTM. Energy 148:461–468. https://doi.org/10.1016/j.energy.2018.01.177
Kumari P, Toshniwal D (2021). Extreme gradient boosting and deep neural network based ensemble learning approach to forecasts hourly solar irradiance. J.Clean Prod 279:123285. https://doi.org/10.1016/j.jclepro.2020.123285
Zang H, Liu L, Sun L, Cheng L, Wei Z, Sun G (2020b) Short term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations. Renew Energy 160:26–41. https://doi.org/10.1016/j.renene.2020.05.150
Zang H, Cheng L, Ding T, Cheung KW,Wei Z, Sun G (2020a) Day ahead photovoltaic power forecasting approach based on deep convolution neural networks and meta Int J Electr power Energy Syst 118:105790. https://doi.org/10.1016/j.ijepes.2019.105790
Wang F, Yu Y, Zhang Z, Li J, Zhen Z, Li K (2018) Wavelet decomposition and convolution LSTM networks based improved deep learning model for solar irradiance forecasting. Appl Sci 8(8):1286. https://doi.org/10.3390/app8081286
Gao B, Huang X, Shi J, Tai Y, Xiao R (2019) Predicting day-ahead solar irradiance through gated recurrent unit using weather forecasting data. J. Renew Sustain Energy 11(4): 043705. https://doi.org/10.1063/1.5110223
Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2): 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
Gao B, Huang X, Shi J, Tai Y, Zhang J (2020) Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks. Renew Energy 162:1665–1683. https://doi.org/10.1016/j.renene.2020.09.141
Huimin Z, Meng S, Wu D, Xinhua Y. A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy 2016; 19(1):14.
Prasad, Ramendra; Ali, Mumtaz; Kwan, Paul; Khan, Huma (2019). Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation. Applied Energy, 236, 778–792. doi:10.1016/j.apenergy.2018.12.034
Qin Q, Lai X, Zou J. Direct multistep wind speed forecasting using LSTM neuralnetwork combining EEMD and fuzzy entropy. Appl Sci 2019; 9(1).
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empiricial mode decomposition and the Hilbert transform for nonlinear and non-stationary time series analysis. Proc A 1998:454(1971): 903–995.
Wu Z, Hunag NE, Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 2009:01(01):1–41.
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9(8):1735–1180.
Zang H, Liu L, Sun L, Cheng L, Wei Z, Sun G (2020b) Short-termglobal horizontal irradiance forecasting based on a hybrid CNNLSTM model with spatiotemporal correlations. Renew Energy 160:26–41. https://doi.org/10.1016/j.renene.2020.05.150
Huang C, Wang L, Lai LL (2019) Data-driven short-term solar irradiance forecasting based on information of neighboring sites. IEEE Trans Ind Electron 66(12):9918–9927. https://doi.org/10.1109/TIE.2018.2856199
Bedi J, Toshniwal D (2019) Deep learning framework to forecast electricity demand. Appl Energy 238:1312–1326. https://doi.org/10.w1016/j.apenergy.2019.01.113
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