Deep learning approach for one-hour ahead forecasting of weather data

References

• Al-Shawwa, M., A. A. Al-Absi, S. A. Hassanein, K. A. Baraka, and S. S. Abu-Naser. 2018. Predicting temperature and humidity in the surrounding environment using artificial neural network. International Journal of Academic Pedagogical Research 2 (9):1–6.
   Google Scholar
• Badescu, V. 1999. Correlations to estimate monthly mean daily solar global irradiation: application to romania. Energy 24 (10):883–93. doi:10.1016/s0360-5442(99)00027-4.
   Web of Science ®Google Scholar
• Bemani, A., A. Baghban, S. Shamshirband, A. Mosavi, P. Csiba, and A. R. Varkonyi-Koczy. 2020. Applying ANN, ANFIS, and LSSVM models for estimation of acid solvent solubility in supercritical CO2. Computers, Materials & Continua. 63(3):1175–204. doi:10.32604/cmc.2020.07723.
   Web of Science ®Google Scholar
• BGE, P., G. M. Jenkins, and G. C. Reinsel. 1994. Time Series Analysis Forecasting and Control. Prentice-Hall, Upper Saddle River. N. J.
   Google Scholar
• Bilgili, M., A. Ilhan, and Ş. Ünal. 2022. Time-series prediction of hourly atmospheric pressure using ANFIS and LSTM approaches. Neural Computing and Applications 34:15633–48. doi:10.1007/s00521-022-07275-5.
   Web of Science ®Google Scholar
• Brown, I. 2012. Influence of seasonal weather and climate variability on crop yields in Scotland. International Journal of Biometeorology 57:605–14. doi:10.1007/s00484-012-0588-9.
   PubMed Web of Science ®Google Scholar
• Chevalier, R. F., G. Hoogenboom, R. W. McClendon, and J. A. Paz. 2010. Support vector regression with reduced training sets for air temperature prediction: A comparison with Artificial Neural Networks. Neural Computing and Applications 20:151–59. doi:10.1007/s00521-010-0363-y.
   Web of Science ®Google Scholar
• Delbari, M., S. Sharifazari, and E. Mohammadi. 2018. Modeling daily soil temperature over diverse climate conditions in Iran—a comparison of multiple linear regression and support vector regression techniques. Theoretical and Applied Climatology 135:991–1001. doi:10.1007/s00704-018-2370-3.
   Web of Science ®Google Scholar
• El-Kenawy, E. M., A. Ibrahim, N. Bailek, K. Bouchouicha, M. A. Hassan, M. Jamei, and N. Al-Ansari. 2021. Sunshine duration measurements and predictions in Saharan Algeria region: An improved ensemble learning approach. Theoretical and Applied Climatology 147 (3–4):1015–31. doi:10.1007/s00704-021-03843-2.
   Web of Science ®Google Scholar
• El-Metwally, M. 2005. Sunshine and global solar radiation estimation at different sites in Egypt. Journal of Atmospheric and Solar-Terrestrial Physics 67 (14):1331–42. doi:10.1016/j.jastp.2005.04.004.
   Web of Science ®Google Scholar
• Erdil, A., and E. Arcaklioglu. 2013. The prediction of meteorological variables using artificial neural network. Neural Computing and Applications 22 (7–8):1677–83. doi:10.1007/s00521-012-1210-0.
   Web of Science ®Google Scholar
• FCM. MATLAB & Simulink. Accessed Oct 5 2022. https://www.mathworks.com/help/fuzzy/fuzzy-c-means-clustering.html
   Google Scholar
• Feng, Y., N. Cui, W. Hao, L. Gao, and D. Gong. 2019. Estimation of soil temperature from meteorological data using different machine learning models. Geoderma 338:67–77. doi:10.1016/j.geoderma.2018.11.044.
   Web of Science ®Google Scholar
• Guo, J., Z. Xie, Y. Qin, L. Jia, and Y. Wang. 2019. Short-term abnormal passenger flow prediction based on the fusion of SVR and LSTM. IEEE Access 7:42946–55. doi:10.1109/access.2019.2907739.
   Web of Science ®Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. Long short-term memory. Neural Computation 9 (8):1735–80. doi:10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Hutapea, M. I., Y. Y. Pratiwi, I. M. Sarkis, I. K. Jaya, and M. Sinambela. 2020. Prediction of relative humidity based on long short-term memory network. AIP Conf Proc 2221(March). doi:10.1063/5.0003171.
   Google Scholar
• Jenkins, G. M., and P. BGE. 1976. Time series analysis forecasting and control. Rev. ed. San Francisco: Holden-Day.
   Google Scholar
• Kaba, K., H. M. Kandirmaz, and M. Avci. 2016. Estimation of daily sunshine duration using support vector machines. International Journal of Green Energy 14:430–41. doi:10.1080/15435075.2016.1265971.
   Web of Science ®Google Scholar
• Kandirmaz, H. M., K. Kaba, and M. Avci. 2014. Estimation of monthly Sunshine duration in Turkey using artificial neural networks. International Journal of Photoenergy 1–9. doi:10.1155/2014/680596.
   Web of Science ®Google Scholar
• Karakuş, O., E. E. Kuruoğlu, and M. A. Altınkaya. 2017. One‐Day ahead wind speed/power prediction based on polynomial autoregressive model. IET Renewable Power Generation 11 (11):1430–39. doi:10.1049/iet-rpg.2016.0972.
   Web of Science ®Google Scholar
• Khosravi, A., L. Machado, and R. O. Nunes. 2018. Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil. Applied Energy 224:550–66. doi:10.1016/j.apenergy.2018.05.043.
   Web of Science ®Google Scholar
• Li, X., Z. Li, W. Huang, and P. Zhou. 2020. Performance of statistical and machine learning ensembles for daily temperature downscaling. Theoretical and Applied Climatology 140:571–88. doi:10.1007/s00704-020-03098-3.
   Web of Science ®Google Scholar
• Liu, Z., and A. Baghban. 2017. Application of LSSVM for biodiesel production using supercritical ethanol solvent. Energy Sources, Part A: Recovery, Utilization, & Environmental Effects 39 (17):1869–74. doi:10.1080/15567036.2017.1380732.
   Web of Science ®Google Scholar
• Li, Q., Y. Zhu, W. Shangguan, X. Wang, L. Li, and F. Yu. 2022. An attention-aware LSTM model for soil moisture and soil temperature prediction. Geoderma 409:115651. doi:10.1016/j.geoderma.2021.115651.
   Web of Science ®Google Scholar
• Mateus, P., J. Catalão, V. B. Mendes, and G. Nico. 2020. An ERA5-based hourly global pressure and temperature (HGPT) model. Remote Sensing 12:1098. doi:10.3390/rs12071098.
   Web of Science ®Google Scholar
• Matuszko, D., and S. Węglarczyk. 2014. Relationship between sunshine duration and air temperature and contemporary global warming. International Journal of Climatology 35:3640–53. doi:10.1002/joc.4238.
   Web of Science ®Google Scholar
• Ma, T., C. Wang, J. Wang, J. Cheng, and X. Chen. 2019. Particle-swarm optimization of ensemble neural networks with negative correlation learning for forecasting short-term wind speed of wind farms in western China. Information Sciences 505:157–82. doi:10.1016/j.ins.2019.07.074.
   Web of Science ®Google Scholar
• Mukhtar, M., A. Oluwasanmi, N. Yimen, Z. Qinxiu, C. C. Ukwuoma, B. Ezurike, and O. Bamisile. 2022. Development and comparison of two novel hybrid neural network models for hourly solar radiation prediction. Applied Sciences. 12(3):1435. doi:10.3390/app12031435.
   Google Scholar
• Nash, J. E., and J. V. Sutcliffe. 1970. River flow forecasting through conceptual models part I — a discussion of principles. Journal of Hydrology 10 (3):282–90. doi:10.1016/0022-1694(70)90255-6.
   Google Scholar
• Ozbek, A., A. Ilhan, M. Bilgili, and B. Sahin. 2022. One-hour ahead wind speed forecasting using deep learning approach. Stochastic Environmental Research and Risk Assessment 36:4311–35. doi:10.1007/s00477-022-02265-4.
   Web of Science ®Google Scholar
• Ozbek, A., Ü. Ş, and M. Bilgili. 2022. Daily average relative humidity forecasting with LSTM neural network and ANFIS approaches. Theoretical and Applied Climatology 150:697–714. doi:10.1007/s00704-022-04181-7.
   Web of Science ®Google Scholar
• Ozbek, A., A. Sekertekin, M. Bilgili, and N. Arslan. 2021. Prediction of 10-min, hourly, and daily atmospheric air temperature: Comparison of LSTM, ANFIS-FCM, and ARMA. Arabian Journal of Geosciences 14: doi:10.1007/s12517-021-06982-y.
   Google Scholar
• Pei, S., H. Qin, Z. Zhang, L. Yao, Y. Wang, C. Wang, Y. Liu, Z. Jiang, J. Zhou, T. Yi, et al. 2019. Wind speed prediction method based on empirical wavelet transform and new cell update long short-term memory network. Energy Conversion and Management 196:779–92. doi:10.1016/j.enconman.2019.06.041.
   Web of Science ®Google Scholar
• Piotrowski, P., D. Baczyński, M. Kopyt, K. Szafranek, P. Helt, and T. Gulczyński. 2019. Analysis of forecasted meteorological data (NWP) for efficient spatial forecasting of wind power generation. Electric Power Systems Research 175:105891. doi:10.1016/j.epsr.2019.105891.
   Web of Science ®Google Scholar
• Qadeer, K., A. Ahmad, M. A. Qyyum, A. S. Nizami, and M. Lee. 2021. Developing machine learning models for relative humidity prediction in air-based energy systems and environmental management applications. Journal of Environmental Management 292:112736. doi:10.1016/j.jenvman.2021.112736.
   PubMed Web of Science ®Google Scholar
• Qing, X., and Y. Niu. 2018. Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148:461–68. doi:10.1016/j.energy.2018.01.177.
   Web of Science ®Google Scholar
• Rahimikhoob, A. 2014. Estimating sunshine duration from other climatic data by Artificial Neural Network for ET0 estimation in an arid environment. Theoretical and Applied Climatology 118 (1–2):1–8. doi:10.1007/s00704-013-1047-1.
   Web of Science ®Google Scholar
• Rjoub, G., J. Bentahar, O. Abdel Wahab, and A. Bataineh. (2019) Deep smart scheduling: A deep learning approach for automated big data scheduling over the cloud 2019 7th International Conference on Future Internet of Things and Cloud (FiCloud). doi: 10.1109/ficloud.2019.00034
   Google Scholar
• Sanikhani, H., R. C. Deo, Z. M. Yaseen, O. Eray, and O. Kisi. 2018. Non-tuned data intelligent model for soil temperature estimation: A new approach. Geoderma 330:52–64. doi:10.1016/j.geoderma.2018.05.030.
   Web of Science ®Google Scholar
• Sarker, I. H., A. S. Kayes, and P. Watters. 2019. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. Journal of Big Data 6 (1). doi:10.1186/s40537-019-0219-y.
   PubMedGoogle Scholar
• Sekertekin, A., M. Bilgili, N. Arslan, A. Yıldırım, K. Celebi, and A. Ozbek. 2021. Short-term air temperature prediction by adaptive neuro-fuzzy inference system (ANFIS) and long short-term memory (LSTM) network. Meteorology and Atmospheric Physics. 133(3):943–59. doi:10.1007/s00703-021-00791-4.
   Web of Science ®Google Scholar
• Solano, E. S., P. Dehghanian, and C. M. Affonso. 2022. Solar radiation forecasting using machine learning and ensemble feature selection. Energies 15 (19):7049. doi:10.3390/en15197049.
   Web of Science ®Google Scholar
• Taylor, K. E. 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres 106 (D7):7183–92. doi:10.1029/2000jd900719.
   Web of Science ®Google Scholar
• Uçkan, İ., and K. M. Khudhur. 2022. Improving of global solar radiation forecast by comparing other meteorological parameter models with sunshine duration models. Environmental Science and Pollution Research 29 (25):37867–81. doi:10.1007/s11356-022-18781-3.
   PubMed Web of Science ®Google Scholar
• Wang, S., X. Yu, L. Liu, J. Huang, T. Ho Wong, and C. Jiang. 2020. An approach for radar quantitative precipitation estimation based on spatiotemporal network. Computers, Materials & Continua 65:459–79. doi:10.32604/cmc.2020.010627.
   Web of Science ®Google Scholar
• Xia, J., X. Ma, W. Wu, B. Huang, and W. Li. 2020. Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity. Journal of Cleaner Production 244:118573. doi:10.1016/j.jclepro.2019.118573.
   Web of Science ®Google Scholar
• Yalçın, S. 2022. Weather parameters forecasting with time series using deep hybrid neural networks. Concurrency & Computation: Practice & Experience 34 (21). doi: 10.1002/cpe.7141.
   Web of Science ®Google Scholar
• Yao, R., L. Wang, X. Huang, L. Li, J. Sun, X. Wu, and W. Jiang. 2020. Developing a temporally accurate air temperature dataset for Mainland China. Science of the Total Environment 706:136037. doi:10.1016/j.scitotenv.2019.136037.
   PubMed Web of Science ®Google Scholar
• Zeynoddin, M., H. Bonakdari, I. Ebtehaj, F. Esmaeilbeiki, B. Gharabaghi, and D. Z. Haghi. 2019. A reliable linear stochastic daily soil temperature forecast model. Soil and Tillage Research 189:73–87. doi:10.1016/j.still.2018.12.023.
   Web of Science ®Google Scholar
• Zhang, Z., L. Ye, H. Qin, Y. Liu, C. Wang, X. Yu, X. Yin, and J. Li. 2019. Wind speed prediction method using shared weight long short-term memory network and gaussian process regression. Applied Energy 247:270–84. doi:10.1016/j.apenergy.2019.04.047.
   Web of Science ®Google Scholar
• Zhang, J., Y. Zhu, X. Zhang, M. Ye, and J. Yang. 2018. Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas. Journal of Hydrology 561:918–29. doi:10.1016/j.jhydrol.2018.04.065.
   Web of Science ®Google Scholar