PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Abuella, M., & Chowdhury, B. (2019). Forecasting of solar power ramp events: A post-processing approach. Renewable Energy, 133, 1380-1392. https://doi.org/10.1016/j.renene.2018.09.005

• Afrasiabi, S., Allahmoradi, S., Salimi, M., Liang, X., & Chung, C. Y. Y. (2022, September 18-20). Nonparametric maximum likelihood probabilistic photovoltaic power generation forecasting based on spatial-temporal deep learning. [Paper presentation]. IEEE Canadian Conference on Electrical and Computer Engineering(CCECE), Halifax, Canada. https://doi.org/10.1109/CCECE49351.2022.9918338

• Ahmed, R., Sreeram, V., Mishra, Y., & Arif, M. D. (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, Article 109792. https://doi.org/10.1016/j.rser.2020.109792

• Alessandrini, S., & McCandless, T. (2020). The schaake shuffle technique to combine solar and wind power probabilistic forecasting. Energies, 13(10), Article 2503. https://doi.org/10.3390/en13102503

• Andrade, J. R., Filipe, J., Reis, M., & Bessa, R. J. (2017). Probabilistic price forecasting for day-ahead and intraday markets: Beyond the statistical model. Sustainability, 9(11), Article 1990. https://doi.org/10.3390/su9111990

• Bai, M., Zhou, Z., Chen, Y., Liu, J., & Yu, D. (2023). Accurate four-hour-ahead probabilistic forecast of photovoltaic power generation based on multiple meteorological variables-aided intelligent optimization of numeric weather prediction data. Earth Science Informatics, 16(3), 2741–2766. https://doi.org/10.1007/s12145-023-01066-9

• Bai, M., Zhou, Z., Li, J., Chen, Y., Liu, J., Zhao, X., & Yu, D. (2024). Deep graph gated recurrent unit network-based spatial–temporal multi-task learning for intelligent information fusion of multiple sites with application in short-term spatial–temporal probabilistic forecast of photovoltaic power. Expert Systems with Applications, 240, Article 122072. https://doi.org/10.1016/j.eswa.2023.122072

• Bakker, K., Whan, K., Knap, W., & Schmeits, M. (2019). Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation. Solar Energy, 191, 138-150. https://doi.org/10.1016/j.solener.2019.08.044

• Bazionis, I. K., & Georgilakis, P. S. (2021). Review of deterministic and probabilistic wind power forecasting: Models, methods, and future research. Electricity, 2(1), 13-47. https://doi.org/10.3390/electricity2010002

• Bhavsar, S., Pitchumani, R., & Ortega-Vazquez, M. A. (2021). Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts. Applied Energy, 293, Article 116964. https://doi.org/10.1016/j.apenergy.2021.116964

• Chen, Z., Chen, Y., Wu, L., Cheng, S., Lin, P., & You, L. (2019). Accurate modeling of photovoltaic modules using a 1-D deep residual network based on I-V characteristics. Energy Conversion and Management, 186, 168-187. https://doi.org/10.1016/j.enconman.2019.02.032

• Cheng, L. L., Zang, H. X., Wei, Z. N., Zhang, F. C., & Sun, G. Q. (2022). Evaluation of opaque deep-learning solar power forecast models towards power-grid applications. Renewable Energy, 198, 960-972. https://doi.org/10.1016/j.renene.2022.08.054

• Chowdhury, M. S., Rahman, K. S., Chowdhury, T., Nuthammachot, N., Techato, K., Akhtaruzzaman, M., Tiong, S. K., Sopian, K., & Amin, N. (2020). An overview of solar photovoltaic panels’ end-of-life material recycling. Energy Strategy Reviews, 27, Article 100431. https://doi.org/10.1016/j.esr.2019.100431

• Chu, Y., Li, M., Coimbra, C. F. M., Feng, D., & Wang, H. (2021). Intra-hour irradiance forecasting techniques for solar power integration: A review. IScience, 24(10), Article 103136. https://doi.org/10.1016/j.isci.2021.103136

• Cui, W., Wan, C., & Song, Y. (2022, July 17-21). Hybrid probabilistic forecasting of photovoltaic power generation considering weather conditions. [Paper presentation]. IEEE Power and Energy Society General Meeting (PESGM), Denver, USA. https://doi.org/10.1109/PESGM48719.2022.9917228

• Devaraj, J., Elavarasan, R. M., Shafiullah, G. M., Jamal, T., Khan, I., Elavarasan, R. M., Shafiullah, G. M., Jamal, T., & Khan, I. (2021). A holistic review on energy forecasting using big data and deep learning models. International Journal of Energy Research, 45(9), 13489-13530. https://doi.org/10.1002/er.6679

• Doelle, O., Klinkenberg, N., Amthor, A., & Ament, C. (2023). Probabilistic intraday PV power forecast using ensembles of deep gaussian mixture density networks. Energies, 16(2), Article 646. https://doi.org/10.3390/en16020646

• Doubleday, K., Van Scyoc Hernandez, V., & Hodge, B. M. (2020). Benchmark probabilistic solar forecasts: Characteristics and recommendations. Solar Energy, 206, 52-67. https://doi.org/10.1016/j.solener.2020.05.051

• Dumas, J., Cointe, C., Fettweis, X., & Cornélusse, B. (2021, June 28- July 2). Deep learning-based multi-output quantile forecasting of PV generation. [Paper presentation]. IEEE Madrid PowerTech, Madrid, Spain. https://doi.org/10.1109/PowerTech46648.2021.9494976

• Dumas, J., Wehenkel, A., Lanaspeze, D., Cornélusse, B., & Sutera, A. (2022). A deep generative model for probabilistic energy forecasting in power systems: Normalizing flows. Applied Energy, 305, Article 117871. https://doi.org/10.1016/j.apenergy.2021.117871

• Feng, C., Liu, Y., & Zhang, J. (2021). A taxonomical review on recent artificial intelligence applications to PV integration into power grids. International Journal of Electrical Power and Energy Systems, 132, Article 107176. https://doi.org/10.1016/j.ijepes.2021.107176

• Huang, Q., & Wei, S. (2020). Improved quantile convolutional neural network with two-stage training for daily-ahead probabilistic forecasting of photovoltaic power. Energy Conversion and Management, 220, Article 113085. https://doi.org/10.1016/j.enconman.2020.113085

• International Energy Agency. (2023). Snapshot of global PV markets 2023 (Report No. IEA-PVPS T1-44:2023). International Energy Agency. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://iea-pvps.org/wp-content/uploads/2023/04/IEA_PVPS_Snapshot_2023.pdf

• Jonler, J. F. F., Brunolottrup, F., Berg, B., Zhang, D., & Chen, K. (2023). Probabilistic forecasts of global horizontal irradiance for solar systems. IEEE Sensors Letters, 7(1), Article 7000104. https://doi.org/10.1109/LSENS.2022.3228783

• Kharlova, E., May, D., & Musilek, P. (2020, July 19-24). Forecasting photovoltaic power production using a deep learning sequence to sequence model with attention. [Paper presentation]. International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom. https://doi.org/10.1109/IJCNN48605.2020.9207573

• Kirkwood, C., Economou, T., Odbert, H., & Pugeault, N. (2021). A framework for probabilistic weather forecast post-processing across models and lead times using machine learning. Philosophical Transactions of the Royal Society A, 379(2194), Article 20200099. https://doi.org/10.1098/rsta.2020.0099

• Kodaira, D., Tsukazaki, K., Kure, T., & Kondoh, J. (2021). Improving forecast reliability for geographically distributed photovoltaic generations. Energies, 14(21), Article 7340. https://doi.org/10.3390/en14217340

• Kumari, P., & Toshniwal, D. (2021). Deep learning models for solar irradiance forecasting: A comprehensive review. Journal of Cleaner Production, 318, Article 128566. https://doi.org/10.1016/j.jclepro.2021.128566

• Lauret, P., David, M., & Pinson, P. (2019). Verification of solar irradiance probabilistic forecasts. Solar Energy, 194, 254-271. https://doi.org/10.1016/j.solener.2019.10.041

• Li, Q., Xu, Y., Chew, B. S. H., Ding, H., & Zhao, G. (2022). An integrated missing-data tolerant model for probabilistic PV power generation forecasting. IEEE Transactions on Power Systems, 37(6), 4447-4459. https://doi.org/10.1109/TPWRS.2022.3146982

• Lin, F., Zhang, Y., Wang, K., Wang, J., & Zhu, M. (2022). Parametric probabilistic forecasting of solar power with fat-tailed distributions and deep neural networks. IEEE Transactions on Sustainable Energy, 13(4), 2133–2147. https://doi.org/10.1109/TSTE.2022.3186517

• Lin, Y., Koprinska, I., & Rana, M. (2021, July 18-22). Temporal convolutional attention neural networks for time series forecasting. [Paper presentation]. International Joint Conference on Neural Networks (IJCNN), Shenzhen, China. https://doi.org/10.1109/IJCNN52387.2021.9534351

• Liu, W., & Xu, Y. (2020). Randomised learning-based hybrid ensemble model for probabilistic forecasting of PV power generation. IET Generation, Transmission and Distribution, 14(24), 5816–5822. https://doi.org/10.1049/iet-gtd.2020.0625

• Liu, Y., Liu, Y., Cai, H., & Zhang, J. (2023). An innovative short-term multihorizon photovoltaic power output forecasting method based on variational mode decomposition and a capsule convolutional neural network. Applied Energy, 343, Article 121139. https://doi.org/10.1016/j.apenergy.2023.121139

• Maraggi, L. M. R., Lake, L. W., & Walsh, M. P. (2021, October 18-21). Bayesian predictive performance assessment of rate-time models for unconventional production forecasting. [Paper presentation]. SPE Europec Featured at 82nd EAGE Conference and Exhibition, Amsterdam, The Netherlands. https://doi.org/10.2118/205151-ms

• Mashlakov, A., Kuronen, T., Lensu, L., Kaarna, A., & Honkapuro, S. (2021). Assessing the performance of deep learning models for multivariate probabilistic energy forecasting. Applied Energy, 285, Article 116405. https://doi.org/10.1016/j.apenergy.2020.116405

• Mellit, A., Massi Pavan, A., Ogliari, E., Leva, S., & Lughi, V. (2020). Advanced methods for photovoltaic output power forecasting: A review. Applied Sciences, 10(2), Article 487.

• Mishra, M., Dash, P. B., Nayak, J., Naik, B., & Swain, S. K. (2020). Deep learning and wavelet transform integrated approach for short-term solar PV power prediction. Measurement: Journal of the International Measurement Confederation, 166, Article 108250. https://doi.org/10.1016/j.measurement.2020.108250

• Mitrentsis, G., & Lens, H. (2022). An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting. Applied Energy, 309, Article 118473. https://doi.org/10.1016/j.apenergy.2021.118473

• Mitrentsis, G., Liu, M., & Lens, H. (2022, June 12-15). Open source tool for probabilistic short-term pv and wind power forecasting. [Paper presentation]. 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Manchester, United Kingdom. https://doi.org/10.1109/PMAPS53380.2022.9810561

• Mittal, A. K., Mathur, K., & Mittal, S. (2022). A review on forecasting the photovoltaic power using machine learning. Journal of Physics: Conference Series, 2286(1), Article 012010. https://doi.org/10.1088/1742-6596/2286/1/012010

• Mpfumali, P., Sigauke, C., Bere, A., & Mulaudzi, S. (2019). Day ahead hourly global horizontal irradiance forecasting—Application to South African data. Energies, 12(18), Article 3569. https://doi.org/10.3390/en12183569

• Mustafa, W. A. (2022). Cervical cancer situation in Malaysia: A systematic literature review. BIOCELL, 46(2), 367–381. https://doi.org/10.32604/biocell.2022.016814

• Panagiotopoulou, V. C., Stavropoulos, P., & Chryssolouris, G. (2022). A critical review on the environmental impact of manufacturing: A holistic perspective. International Journal of Advanced Manufacturing Technology, 118(1–2), 603–625. https://doi.org/10.1007/s00170-021-07980-w

• Park, S., Park, S., & Hwang, E. (2020, February 19-22). Normalized residue analysis for deep learning based probabilistic forecasting of photovoltaic generations. [Paper presentation]. IEEE International Conference on Big Data and Smart Computing (BigComp), Busan, Korea. https://doi.org/10.1109/BigComp48618.2020.00-20

• Pazikadin, A. R., Rifai, D., Ali, K., Malik, M. Z., Abdalla, A. N., & Faraj, M. A. (2020). Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend. Science of the Total Environment, 715, Article 136848. https://doi.org/10.1016/j.scitotenv.2020.136848

• Perera, M., De Hoog, J., Bandara, K., & Halgamuge, S. (2022). Multi-resolution, multi-horizon distributed solar PV power forecasting with forecast combinations. Expert Systems with Applications, 205, Article 117690. https://doi.org/10.1016/j.eswa.2022.117690

• Phan, Q. T. Q. D., Wu, Y. K., & Phan, Q. T. Q. D. (2024). Enhancing one-day-ahead probabilistic solar power forecast with a hybrid transformer-LUBE model and missing data imputation. IEEE Transactions on Industry Applications, 60(1), 1396–1408. https://doi.org/10.1109/TIA.2023.3325798

• Polo, J., Martín-Chivelet, N., Alonso-Abella, M., Sanz-Saiz, C., Cuenca, J., & Cruz, M. D. L. (2023). Exploring the PV power forecasting at building façades using gradient boosting methods. Energies, 16(3), Article 1495. https://doi.org/10.3390/en16031495

• Qiao, J., Pu, T. J., & Wang, X. Y. (2021). Renewable scenario generation using controllable generative adversarial networks with transparent latent space. CSEE Journal of Power and Energy Systems, 7(1), 66-77. https://doi.org/10.17775/CSEEJPES.2020.00700

• Rajagukguk, R. A., Ramadhan, R. A. A., & Lee, H. J. (2020). A review on deep learning models for forecasting time series data of solar irradiance and photovoltaic power. Energies, 13(24), Article 6623. https://doi.org/10.3390/en13246623

• Salisu, A. A., Gupta, R., & Ogbonna, A. E. (2021). Point and density forecasting of macroeconomic and financial uncertainties of the USA. Journal of Forecasting, 40(4), 700-707. https://doi.org/10.1002/for.2740

• Sansine, V., Ortega, P., Hissel, D., & Hopuare, M. (2022). Solar irradiance probabilistic forecasting using machine learning, metaheuristic models and numerical weather predictions. Sustainability, 14(22), Article 15260. https://doi.org/10.3390/su142215260

• Shafiullah, M., Ahmed, S. D., & Al-Sulaiman, F. A. (2022). Grid integration challenges and solution strategies for solar PV systems: A review. IEEE Access, 10, 52233-52257. https://doi.org/10.1109/ACCESS.2022.3174555

• Shi, J., Wang, Y., Zhou, Y., Ma, Y., Gao, J., Wang, S., & Fu, Z. (2023). Bayesian optimization - LSTM modeling and time frequency correlation mapping based probabilistic forecasting of ultra-short-term photovoltaic power outputs. IEEE Transactions on Industry Applications, 60(2), 2422-2430. https://doi.org/10.1109/TIA.2023.3334700

• Sun, M., He, L., & Zhang, J. (2022). Deep learning-based probabilistic anomaly detection for solar forecasting under cyberattacks. International Journal of Electrical Power and Energy Systems, 137, Article 107752. https://doi.org/10.1016/j.ijepes.2021.107752

• Thaker, J., & Höller, R. (2022). A comparative study of time series forecasting of solar energy based on irradiance classification. Energies, 15(8), Article 2837. https://doi.org/10.3390/en15082837

• Toubeau, J. F., Bottieau, J., Vallee, F., & De Greve, Z. (2019). Deep learning-based multivariate probabilistic forecasting for short-term scheduling in power markets. IEEE Transactions on Power Systems, 34(2), 1203-1215. https://doi.org/10.1109/TPWRS.2018.2870041

• Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (2019). A review of deep learning for renewable energy forecasting. Energy Conversion and Management, 198, Article 111799. https://doi.org/10.1016/j.enconman.2019.111799

• Wang, J., Qian, Z., Wang, J., & Pei, Y. (2020). Hour-ahead photovoltaic power forecasting using an analog plus neural network ensemble method. Energies, 13(12), Article 3259. https://doi.org/10.3390/en13123259

• Wang, W., Yang, D., Hong, T., & Kleissl, J. (2022). An archived dataset from the ECMWF ensemble prediction system for probabilistic solar power forecasting. Solar Energy, 248, 64–75. https://doi.org/10.1016/j.solener.2022.10.062

• Wen, Y., AlHakeem, D., Mandal, P., Chakraborty, S., Wu, Y. K., Senjyu, T., Paudyal, S., & Tseng, T. L. (2020). Performance evaluation of probabilistic methods based on bootstrap and quantile regression to quantify PV power point forecast uncertainty. IEEE transactions on neural networks and learning systems, 31(4), 1134-1144. https://doi.org/10.1109/TNNLS.2019.2918795

• Yagli, G. M., Yang, D., & Srinivasan, D. (2020). Reconciling solar forecasts: Probabilistic forecasting with homoscedastic Gaussian errors on a geographical hierarchy. Solar Energy, 210, 59–67. https://doi.org/10.1016/j.solener.2020.06.005

• Yang, D. (2020). Reconciling solar forecasts: Probabilistic forecast reconciliation in a nonparametric framework. Solar Energy, 210, 49-58. https://doi.org/10.1016/j.solener.2020.03.095

• Yu, W., Liu, G., Zhu, L., & Yu, W. (2020). Convolutional neural network with feature reconstruction for monitoring mismatched photovoltaic systems. Solar Energy, 212, 169-177. https://doi.org/10.1016/j.solener.2020.09.026

• Zafar, M. H., Khan, N. M., Mansoor, M., Mirza, A. F., Moosavi, S. K. R., Sanfilippo, F., Zafar, M. H., Khan, N. M., Mansoor, M., Mirza, A. F., Moosavi, S. K. R., & Sanfilippo, F. (2022). Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems. Energy Conversion and Management, 258, Article 115564. https://doi.org/10.1016/j.enconman.2022.115564

• Zang, H., Cheng, L., Ding, T., Cheung, K. W., Wei, Z., & Sun, G. (2020). Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning. International Journal of Electrical Power and Energy Systems, 118, Article 105790. https://doi.org/10.1016/j.ijepes.2019.105790

• Zhou, N., Xu, X., Yan, Z., & Shahidehpour, M. (2022). Spatio-temporal probabilistic forecasting of photovoltaic power based on monotone broad learning system and copula theory. IEEE Transactions on Sustainable Energy, 13(4), 1874–1885. https://doi.org/10.1109/TSTE.2022.3174012