- Alanis, A.Y.; Sanchez, O.D.; Alvarez, J.G. Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks. Mathematics 2021, 9, 1075. [CrossRef]
Paper not yet in RePEc: Add citation now
- Awe, O.; Okeyinka, A.; Fatokun, J.O. An Alternative Algorithm for ARIMA Model Selection. In Proceedings of the 2020 IEEE International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS), Ayobo, Nigeria, 14–16 October 2020; pp. 1–4. [CrossRef]
Paper not yet in RePEc: Add citation now
Chodakowska, E.; Nazarko, J.; Nazarko, Ł.; Rabayah, H.S.; Abendeh, R.M.; Alawneh, R. ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations. Energies 2023, 16, 5029. [CrossRef]
- Ding, S.; Hua, X.; Yu, J. An overview on nonparallel hyperplane support vector machine algorithms. Neural Comput. Appl. 2014, 25, 975–982. [CrossRef]
Paper not yet in RePEc: Add citation now
- Farsi, M.; Hosahalli, D.; Manjunatha, B.R.; Gad, I.; Atlam, E.-S.; Ahmed, A.; Elmarhomy, G.; Elmarhoumy, M.; Ghoneim, O.A. Parallel Genetic Algorithms for Optimizing the SARIMA Model for Better Forecasting of the NCDC Weather Data. Alex. Eng. J. 2021, 60, 1299–1316. [CrossRef]
Paper not yet in RePEc: Add citation now
- Graf, R.; Zhu, S.; Sivakumar, B. Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach. J. Hydrol. 2019, 578, 124115. [CrossRef]
Paper not yet in RePEc: Add citation now
- Hamilton, J.D. Time Series Analysis; Princeton University Press: Princeton, NJ, USA, 2020.
Paper not yet in RePEc: Add citation now
Hannan, M.A.; Al-Shetwi, A.Q.; Mollik, M.S.; Ker, P.J.; Mannan, M.; Mansor, M.; Al-Masri, H.M.K.; Mahlia, T.M.I. Wind energy conversions, controls, and applications: A review for sustainable technologies and directions. Sustainability 2023, 15, 3986. [CrossRef]
Hasan, M.M.; Hossain, S.; Mofijur, M.; Kabir, Z.; Badruddin, I.A.; Yunus Khan, T.M.; Jassim, E. Harnessing Solar Power: A Review of Photovoltaic Innovations, Solar Thermal Systems, and the Dawn of Energy Storage Solutions. Energies 2023, 16, 6456. [CrossRef]
Helerea, E.; Calin, M.D.; Musuroi, C. Water Energy Nexus and Energy Transition—A Review. Energies 2023, 16, 1879. [CrossRef]
- Izo-nin, I.; Tkachenko, R.; Shakhovska, N.; Lotoshynska, N. The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach. Symmetry 2021, 13, 612. [CrossRef] Energies 2024, 17, 4803 18 of 18
Paper not yet in RePEc: Add citation now
- Jamróz, D.; Niedoba, T.; Surowiak, A.; Tumidajski, T.; Szostek, R.; Gajer, M. Application of Multi-Parameter Data Visualization by Means of Multidimensional Scaling to Evaluate Possibility of Coal Gasification. Arch. Min. Sci. 2017, 62. [CrossRef]
Paper not yet in RePEc: Add citation now
- Jenkins, G.M.; Box, G.E.P. Time Series Analysis: Forecasting and Control; Holden-Day: San Francisco, CA, USA, 1976.
Paper not yet in RePEc: Add citation now
- Kaur, J.; Parmar, K.S.; Singh, S. Autoregressive models in environmental forecasting time series: A theoretical and application review. Environ. Sci. Pollut. Res. 2023, 30, 19617–19641. [CrossRef]
Paper not yet in RePEc: Add citation now
- Lai, J.-P.; Chang, Y.-M.; Chen, C.-H.; Pai, P.-F. A Survey of Machine Learning Models in Renewable Energy Predictions. Appl. Sci. 2020, 10, 5975. [CrossRef]
Paper not yet in RePEc: Add citation now
Li, Z.; Zuo, A.; Li, C. Predicting Raw Milk Price Based on Depth Time Series Features for Consumer Behavior Analysis. Sustainability 2023, 15, 6647. [CrossRef]
Liu, X.; Lin, Z.; Feng, Z. Short-term Offshore Wind Speed Forecast by Seasonal ARIMA: A Comparison Against GRU and LSTM. Energy 2021, 227, 120492. [CrossRef]
- Manowska, A.; Rybak, A.; Dylong, A.; Pielot, J. Forecasting of Natural Gas Consumption in Poland Based on ARIMA-LSTM Hybrid Model. Energies 2021, 14, 8597. [CrossRef]
Paper not yet in RePEc: Add citation now
- Michalak, A.; Wolniak, R. The innovativeness of the country and the renewables and non-renewables in the energy mix on the example of European Union. J. Open Innov. Technol. Mark. Complex. 2023, 9, 100061. [CrossRef]
Paper not yet in RePEc: Add citation now
- Mosavi, A.; Salimi, M.; Faizollahzadeh Ardabili, S.; Rabczuk, T.; Shamshirband, S.; Varkonyi-Koczy, A.R. State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. Energies 2019, 12, 1301. [CrossRef]
Paper not yet in RePEc: Add citation now
- Nokeri, T.C. Forecasting Using ARIMA, SARIMA, and the Additive Model. In Implementing Machine Learning for Finance; Apress: Berkeley, CA, USA, 2021. [CrossRef]
Paper not yet in RePEc: Add citation now
- Pourasl, H.H.; Vatankhah Barenji, R.; Khojastehnezhad, V.M. Solar energy status in the world: A comprehensive review. Energy Rep. 2023, 10, 3474–3493. [CrossRef]
Paper not yet in RePEc: Add citation now
- Sagheer, A.; Kotb, M. Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing 2019, 323, 203–213. [CrossRef]
Paper not yet in RePEc: Add citation now
Sayed, E.T.; Olabi, A.G.; Alami, A.H.; Radwan, A.; Mdallal, A.; Rezk, A.; Abdelkareem, M.A. Renewable Energy and Energy Storage Systems. Energies 2023, 16, 1415. [CrossRef]
- Sirisha, U.M.; Belavagi, M.C.; Attigeri, G. Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison. IEEE Access 2022, 10, 124715–124727. [CrossRef]
Paper not yet in RePEc: Add citation now
- Szostek, K. Estimation of the power of a geothermal energy recovery system that uses a heat exchanger. Renew. Energy 2024, 220,
Paper not yet in RePEc: Add citation now
- Szostek, R. An estimation of the geothermal energy sources for generating electricity. In Analysis and Simulation of Electrical and Computer Systems, 2nd ed.; Goł˛ ebiowski, L., Mazur, D., Eds.; Springer: Cham, Switzerland, 2015; Volume 324, pp. 154–196. [CrossRef]
Paper not yet in RePEc: Add citation now
Turco, E.; Bazzana, D.; Rizzati, M.; Ciola, E.; Vergalli, S. Energy price shocks and stabilization policies in the MATRIX model. Energy Policy 2023, 177, 113567. [CrossRef]
- Uzair, M.; Shah, I.; Ali, S. An Adaptive Strategy for Wind Speed Forecasting Under Functional Data Horizon: A Way Toward Enhancing Clean Energy. IEEE Access 2024, 12, 68730–68746. [CrossRef]
Paper not yet in RePEc: Add citation now
- Vapnik, V.; Golowich, S.; Smola, A. Support vector method for function approximation, regression estimation and signal processing. In Advances in Neural Information Processing Systems 9; MIT Press: Cambridge, MA, USA, 1996.
Paper not yet in RePEc: Add citation now
- Wang, H.; Lei, Z.; Zhang, X.; Zhou, B.; Peng, J. A review of deep learning for renewable energy forecasting. Energy Convers. Manag. 2019, 198, 111799. [CrossRef]
Paper not yet in RePEc: Add citation now
- Wu, Z.; Pan, S.; Long, G.; Jiang, J.; Chang, X.; Zhang, C. Connecting the dots: Multivariate time series forecasting with deep learning: A survey. Philos. Trans. R. Soc. A 2021, 379, 20200209.
Paper not yet in RePEc: Add citation now