References
- Abdel-Nasser, M., and K. Mahmoud. 2019. Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Computing and Applications 31 (7):2727–40. doi:https://doi.org/10.1007/s00521-017-3225-z.
- Abdel-Nasser, M., K. Mahmoud, and M. Lehtonen. 2021 March. Reliable solar irradiance forecasting approach based on choquet integral and deep LSTMs. IEEE Transactions on Industrial Informatics 17(3):1873–81. doi:https://doi.org/10.1109/TII.2020.2996235.
- Ahmed, R., V. Sreeram, Y. Mishra, and M. D. Arif. 2020. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renewable and Sustainable Energy Reviews 124(June):109792. 2019. doi:https://doi.org/10.1016/j.rser.2020.109792.
- Ahmedabad Climate Ahmedabad Temperatures Ahmedabad Weather Averages. https://www.ahmadabad.climatemps.com/ (accessed April. 01, 2021).
- AlKandari, M., and I. Ahmad. 2019. Solar power generation forecasting using ensemble approach based on deep learning and statistical methods. Applied Computing and Informatics. Elsevier B.V. doi:https://doi.org/10.1016/j.aci.2019.11.002.
- Bedi, J., and D. Toshniwal. 2019. Deep learning framework to forecast electricity demand. Applied Energy 238 (March):1312–26. doi:https://doi.org/10.1016/j.apenergy.2019.01.113.
- Bouzgou, H., and C. A. Gueymard. 2019. Fast short-term global solar irradiance forecasting with wrapper mutual information. Renewable Energy 133 (April):1055–65. doi:https://doi.org/10.1016/j.renene.2018.10.096.
- Cheng, H., X. Ding, W. Zhou, and R. Ding. 2019. A hybrid electricity price forecasting model with Bayesian optimization for German energy exchange. International Journal of Electrical Power and Energy Systems 110 (September):653–66. doi:https://doi.org/10.1016/j.ijepes.2019.03.056.
- Deng, Y., B. Wang, and Z. Lu. 2020. A hybrid model based on data preprocessing strategy and error correction system for wind speed forecasting. Energy Conversion and Management 212 (May):112779. doi:https://doi.org/10.1016/J.ENCONMAN.2020.112779.
- Dong, N., J. F. Chang, A. G. Wu, and Z. K. Gao. 2020. A novel convolutional neural network framework based solar irradiance prediction method. International Journal of Electrical Power and Energy Systems 114 (January):105411. doi:https://doi.org/10.1016/j.ijepes.2019.105411.
- Dragomiretskiy, K., and D. Zosso. 2014 February. Variational mode decomposition. IEEE Transactions on Signal Processing 62(3):531–44. doi:https://doi.org/10.1109/TSP.2013.2288675.
- Fouilloy, A., C. Voyant, G. Notton, F. Motte, C. Paoli, M.-L. Nivet, E. Guillot, J.-L. Duchaud et al, . 2018, December. Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability. Energy 165:620–29. doi:https://doi.org/10.1016/j.energy.2018.09.116.
- Gao, B., X. Huang, J. Shi, Y. Tai, and R. Xiao. 2019 July. Predicting day-ahead solar irradiance through gated recurrent unit using weather forecasting data. Journal of Renewable and Sustainable Energy 11(4):043705. doi:https://doi.org/10.1063/1.5110223.
- Gao, B., X. Huang, J. Shi, Y. Tai, and J. Zhang. 2020. Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks. Renewable Energy 162 (December):1665–83. doi:https://doi.org/10.1016/j.renene.2020.09.141.
- Guo, X., C. Zhu, J. Hao, S. Zhang, and L. Zhu. 2021 June. A hybrid method for short-term wind speed forecasting based on Bayesian optimization and error correction. Journal of Renewable and Sustainable Energy 13(3):036101. doi:https://doi.org/10.1063/5.0048686.
- Hochreiter, S., and J. Schmidhuber. 1997 November. Long short-term memory. Neural Computation 9(8):1735–80. doi:https://doi.org/10.1162/neco.1997.9.8.1735.
- Huang, X., Q. Li, Y. Tai, Z. Chen, J. Zhang, J. Shi, B. Gao, W. Liu . 2021, June. Hybrid deep neural model for hourly solar irradiance forecasting. Renewable Energy 171:1041–60. doi:https://doi.org/10.1016/j.renene.2021.02.161.
- Huang, Y., L. Yang, S. Liu, and G. Wang. 2019 May. Multi-step wind speed forecasting based on ensemble empirical mode decomposition, long short term memory network and error correction strategy. Energies 12(10):1822. doi:https://doi.org/10.3390/en12101822.
- Kulshrestha, A., V. Krishnaswamy, and M. Sharma. 2020. Bayesian BILSTM approach for tourism demand forecasting. Annals of Tourism Research 83 (July):102925. doi:https://doi.org/10.1016/j.annals.2020.102925.
- Kumari, P., and D. Toshniwal. 2021. Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance. Journal of Cleaner Production 279 (January):123285. doi:https://doi.org/10.1016/j.jclepro.2020.123285.
- Li, G., X. Ma, and H. Yang. 2018. A hybrid model for forecasting sunspots time series based on variational mode decomposition and backpropagation neural network improved by firefly algorithm. Computational Intelligence and Neuroscience 2018:1–9. doi:https://doi.org/10.1155/2018/3713410.
- Li, C., Y. Zhang, G. Zhao, and Y. Ren. 2020a August. Hourly solar irradiance prediction using deep BiLSTM network. Earth Science Informatics 1–11. doi:https://doi.org/10.1007/s12145-020-00511-3.
- Li, P., K. Zhou, X. Lu, and S. Yang. 2020b. A hybrid deep learning model for short-term PV power forecasting. Applied Energy 259 (February):114216. doi:https://doi.org/10.1016/j.apenergy.2019.114216.
- Liu, H., and C. Chen. 2019. Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction. Applied Energy 254 (November):113686. doi:https://doi.org/10.1016/j.apenergy.2019.113686.
- Mahmoud, K., M. Abdel-Nasser, E. Mustafa, and Z. M. Ali. 2020, January. Improved salp–swarm optimizer and accurate forecasting model for dynamic economic dispatch in sustainable power systems. ( 2020) Sustainability 12 (2):576. doi: https://doi.org/10.3390/SU12020576.
- Mahmoud, K., and M. Lehtonen. 2021 November. Comprehensive analytical expressions for assessing and maximizing technical benefits of photovoltaics to distribution systems. IEEE Transactions on Smart Grid 12(6):4938–49. doi:https://doi.org/10.1109/TSG.2021.3097508.
- Mejia, J. F., M. Giordano, and E. Wilcox. 2018. Conditional summertime day-ahead solar irradiance forecast. Solar Energy 163 (March):610–22. doi:https://doi.org/10.1016/j.solener.2018.01.094.
- Mishra, M., P. Byomakesha Dash, J. Nayak, B. Naik, and S. Kumar Swain. 2020. Deep learning and wavelet transform integrated approach for short-term solar PV power prediction. Measurement: Journal of the International Measurement Confederation 166 (December):108250. doi:https://doi.org/10.1016/j.measurement.2020.108250.
- Özger, M., E. E. Başakın, Ö. Ekmekcioğlu, and V. Hacısüleyman. 2020. Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction. Computers and Electronics in Agriculture 179 (December):105851. doi:https://doi.org/10.1016/j.compag.2020.105851.
- Pearre, N. S., and L. G. Swan. 2018. Statistical approach for improved wind speed forecasting for wind power production. Sustainable Energy Technologies and Assessments 27 (June):180–91. doi:https://doi.org/10.1016/j.seta.2018.04.010.
- Peng, T., C. Zhang, J. Zhou, and M. S. Nazir. 2021. An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting. Energy 221 (April):119887. doi:https://doi.org/10.1016/j.energy.2021.119887.
- Preda, S., S. V. Oprea, A. Bâra, and A. Belciu. 2018. PV forecasting using support vector machine learning in a big data analytics context. Symmetry 10 (12):748. doi:https://doi.org/10.3390/sym10120748.
- Qing, X., and Y. Niu. 2018. Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148 (April):461–68. doi:https://doi.org/10.1016/j.energy.2018.01.177.
- Qu, Z., W. Mao, K. Zhang, W. Zhang, and Z. Li. 2019. Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network. Renewable Energy 133 (April):919–29. doi:https://doi.org/10.1016/j.renene.2018.10.043.
- Sahu, R. K., B. Shaw, J. R. Nayak, and S. Shashikant. 2021. Short/medium term solar power forecasting of Chhattisgarh state of India using modified TLBO optimized ELM. Engineering Science and Technology, an International Journal, Mar 24 (5):1180–200. doi:https://doi.org/10.1016/J.JESTCH.2021.02.016.
- Singla, P., M. Duhan, and S. Saroha. 2021a March. A comprehensive review and analysis of solar forecasting techniques. Frontiers in Energy 1–37. doi:https://doi.org/10.1007/s11708-021-0722-7.
- Singla, P., M. Duhan, and S. Saroha. 2021b, November. An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network. ( 2021) Earth Science Informatics 1:1–16. doi: https://doi.org/10.1007/S12145-021-00723-1.
- Sun, S., S. Wang, G. Zhang, and J. Zheng. 2018. A decomposition-clustering-ensemble learning approach for solar radiation forecasting. Solar Energy 163 (March):189–99. doi:https://doi.org/10.1016/j.solener.2018.02.006.
- Torres, M. E., M. A. Colominas, G. Schlotthauer, and P. Flandrin, “A complete ensemble empirical mode decomposition with adaptive noise,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Prague, Czech Republic, 2011, pp. 4144–47. doi: https://doi.org/10.1109/ICASSP.2011.5947265.
- Wang, J., and Y. Li. 2018. Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy. Applied Energy 230 (November):429–43. doi:https://doi.org/10.1016/j.apenergy.2018.08.114.
- Wang, F., Y. Yu, Z. Zhang, J. Li, Z. Zhen, and K. Li. 2018 August. Wavelet decomposition and convolutional LSTM networks based improved deep learning model for solar irradiance forecasting. Applied Sciences 8(8):1286. doi:https://doi.org/10.3390/app8081286.
- Wu, Z., and N. E. Huang. 2009 January. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis 1(1):1–41. doi:https://doi.org/10.1142/S1793536909000047.
- Wu, K., Wu, J., Feng, L., Yang, B., Liang, R., Yang, S., Zhao, R . 2020, September. An attention‐based CNN‐LSTM‐BiLSTM model for short‐term electric load forecasting in integrated energy system. International Transactions on Electrical Energy Systems. doi: https://doi.org/10.1002/2050-7038.12637.
- Zeroual, A., F. Harrou, A. Dairi, and Y. Sun. 2020. Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study. Chaos, Solitons and Fractals 140 (November):110121. doi:https://doi.org/10.1016/j.chaos.2020.110121.
- Zhang, B., H. Zhang, G. Zhao, and J. Lian. 2020. Constructing a PM2.5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks. Environmental Modelling and Software 124 (February):104600. doi:https://doi.org/10.1016/j.envsoft.2019.104600.