Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Computational Fluid Dynamics
2.2. WEC Geometry
Different Components of the Wave Converter
2.3. Boundary Conditions and Network Generation
2.4. Grid Independency
2.5. Assumptions
Governing Equations of Fluid
3. Results
3.1. Results of Data Optimization with Preprocessing
3.2. Comparative Analysis of Numerical Solution and Prediction of Values by Artificial Intelligence Algorithms
3.3. Evaluation of Simulation and Validation of Results
3.4. Hydrogen Production from Waves
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Kalbasi, R.; Jahangiri, M.; Mosavi, A.; Dehshiri, S.J.H.; Ebrahimi, S.; Etezadi, Z.A.-S.; Karimipour, A. Finding the best station in Belgium to use residential-scale solar heating, One-year dynamic simulation with considering all system losses: Economic analysis of using ETSW. Sustain. Energy Technol. Assess. 2021, 45, 101097. [Google Scholar] [CrossRef]
- Megura, M.; Gunderson, R. Better poison is the cure? Critically examining fossil fuel companies, climate change framing, and corporate sustainability reports. Energy Res. Soc. Sci. 2021, 85, 102388. [Google Scholar] [CrossRef]
- Holechek, J.L.; Geli, H.M.E.; Sawalhah, M.N.; Valdez, R. A Global Assessment: Can Renewable Energy Replace Fossil Fuels by 2050? Sustainability 2022, 14, 4792. [Google Scholar] [CrossRef]
- Ahmad, M.; Kumar, A.; Ranjan, R. Recent Developments of Tidal Energy as Renewable Energy: An Overview. River Coast. Eng. 2022, 11, 329–343. [Google Scholar] [CrossRef]
- Amini, E.; Mehdipour, H.; Faraggiana, E.; Golbaz, D.; Mozaffari, S.; Bracco, G.; Neshat, M. Optimization of hydraulic power take-off system settings for point absorber wave energy converter. Renew. Energy 2022, 194, 938–954. [Google Scholar] [CrossRef]
- Claywell, R.; Nadai, L.; Felde, I.; Ardabili, S.; Mosavi, A. Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction. Entropy 2020, 22, 1192. [Google Scholar] [CrossRef] [PubMed]
- McLeod, I.; Ringwood, J.V. Powering data buoys using wave energy: A review of possibilities. J. Ocean Eng. Mar. Energy 2022, 8, 417–432. [Google Scholar] [CrossRef]
- Olsson, G. Water Interactions: A Systemic View: Why We Need to Comprehend the Water-Climate-Energy-Food-Economics-Lifestyle Connections; IWA Publishing: London, UK, 2022. [Google Scholar]
- Malkowska, A.; Malkowski, A. Green Energy in the Political Debate. In Green Energy; Springer: Cham, Switzerland, 2023; pp. 17–39. [Google Scholar]
- Mayon, R.; Ning, D.; Ding, B.; Sergiienko, N.Y. Wave energy converter systems–status and perspectives. In Modelling and Optimisation of Wave Energy Converters; CRC Press: Boca Raton, FL, USA, 2022; pp. 3–58. [Google Scholar]
- Available online: https://www.offshore-energy.biz/uk-ecotricity-introduces-wave-power-device-searaser/ (accessed on 27 September 2022).
- Mousavi, S.M.; Ghasemi, M.; Dehghan Manshadi, M.; Mosavi, A. Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory. Mathematics 2021, 9, 871. [Google Scholar] [CrossRef]
- Mega, V. The Energy Race to Decarbonisation. In Human Sustainable Cities; Springer: Cham, Switzerland, 2022; pp. 105–141. [Google Scholar]
- Li, R.; Tang, B.-J.; Yu, B.; Liao, H.; Zhang, C.; Wei, Y.-M. Cost-optimal operation strategy for integrating large scale of renewable energy in China’s power system: From a multi-regional perspective. Appl. Energy 2022, 325, 119780. [Google Scholar] [CrossRef]
- Ardabili, S.; Abdolalizadeh, L.; Mako, C.; Torok, B. Systematic Review of Deep Learning and Machine Learning for Building Energy. Front. Energy Res. 2022, 10, 77–98. [Google Scholar] [CrossRef]
- Penalba, M.; Aizpurua, J.I.; Martinez-Perurena, A.; Iglesias, G. A data-driven long-term metocean data forecasting approach for the design of marine renewable energy systems. Renew. Sustain. Energy Rev. 2022, 167, 112751. [Google Scholar] [CrossRef]
- Torabi, M.; Hashemi, S.; Saybani, M.R.; Shamshirband, S.; Mosavi, A. A Hybrid clustering and classification technique for forecasting short-term energy consumption. Environ. Prog. Sustain. Energy 2018, 38, 66–76. [Google Scholar] [CrossRef] [Green Version]
- Rivera, F.P.; Zalamea, J.; Espinoza, J.L.; Gonzalez, L.G. Sustainable use of spilled turbinable energy in Ecuador: Three different energy storage systems. Renew. Sustain. Energy Rev. 2021, 156, 112005. [Google Scholar] [CrossRef]
- Raza, S.A.; Jiang, J. Mathematical Foundations for Balancing Single-Phase Residential Microgrids Connected to a Three-Phase Distribution System. IEEE Access 2022, 10, 5292–5303. [Google Scholar] [CrossRef]
- Takach, M.; Sarajlić, M.; Peters, D.; Kroener, M.; Schuldt, F.; von Maydell, K. Review of Hydrogen Production Techniques from Water Using Renewable Energy Sources and Its Storage in Salt Caverns. Energies 2022, 15, 1415. [Google Scholar] [CrossRef]
- Lv, Z.; Li, W.; Wei, J.; Ho, F.; Cao, J.; Chen, X. Autonomous Chemistry Enabling Environment-Adaptive Electrochemical Energy Storage Devices. CCS Chem. 2022, 7, 1–19. [Google Scholar] [CrossRef]
- Manshadi, M.D.; Mousavi, M.; Soltani, M.; Mosavi, A.; Kovacs, L. Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System. Energies 2022, 15, 9484. [Google Scholar] [CrossRef]
- Ishaq, H.; Dincer, I.; Crawford, C. A review on hydrogen production and utilization: Challenges and opportunities. Int. J. Hydrogen Energy 2022, 47, 26238–26264. [Google Scholar] [CrossRef]
- Maguire, J.F.; Woodcock, L.V. On the Thermodynamics of Aluminum Cladding Oxidation: Water as the Catalyst for Spontaneous Combustion. J. Fail. Anal. Prev. 2022, 22, 1771–1775. [Google Scholar] [CrossRef]
- Mohammadi, M.-R.; Hadavimoghaddam, F.; Pourmahdi, M.; Atashrouz, S.; Munir, M.T.; Hemmati-Sarapardeh, A.; Mosavi, A.H.; Mohaddespour, A. Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state. Sci. Rep. 2021, 11, 1–20. [Google Scholar] [CrossRef]
- Ma, S.; Qin, J.; Xiu, X.; Wang, S. Design and performance evaluation of an underwater hybrid system of fuel cell and battery. Energy Convers. Manag. 2022, 262, 115672. [Google Scholar] [CrossRef]
- Ahamed, R.; McKee, K.; Howard, I. A Review of the Linear Generator Type of Wave Energy Converters’ Power Take-Off Systems. Sustainability 2022, 14, 9936. [Google Scholar] [CrossRef]
- Nejad, H.D.; Nazari, M.; Nazari, M.; Mardan, M.M.S. Fuzzy State-Dependent Riccati Equation (FSDRE) Control of the Reverse Osmosis Desalination System With Photovoltaic Power Supply. IEEE Access 2022, 10, 95585–95603. [Google Scholar] [CrossRef]
- Zou, S.; Zhou, X.; Khan, I.; Weaver, W.W.; Rahman, S. Optimization of the electricity generation of a wave energy converter using deep reinforcement learning. Ocean Eng. 2021, 244, 110363. [Google Scholar] [CrossRef]
- Wu, J.; Qin, L.; Chen, N.; Qian, C.; Zheng, S. Investigation on a spring-integrated mechanical power take-off system for wave energy conversion purpose. Energy 2022, 245, 123318. [Google Scholar] [CrossRef]
- Papini, G.; Piuma, F.J.D.; Faedo, N.; Ringwood, J.V.; Mattiazzo, G. Nonlinear Model Reduction by Moment-Matching for a Point Absorber Wave Energy Conversion System. J. Mar. Sci. Eng. 2022, 10, 656. [Google Scholar] [CrossRef]
- Forbush, D.D.; Bacelli, G.; Spencer, S.J.; Coe, R.G.; Bosma, B.; Lomonaco, P. Design and testing of a free floating dual flap wave energy converter. Energy 2021, 240, 122485. [Google Scholar] [CrossRef]
- Rezaei, M.A.; Nayeripour, M.; Hu, J.; Band, S.S.; Mosavi, A.; Khooban, M.-H. A New Hybrid Cascaded Switched-Capacitor Reduced Switch Multilevel Inverter for Renewable Sources and Domestic Loads. IEEE Access 2022, 10, 14157–14183. [Google Scholar] [CrossRef]
- Lin, Z.; Cheng, L.; Huang, G. Electricity consumption prediction based on LSTM with attention mechanism. IEEJ Trans. Electr. Electron. Eng. 2019, 15, 556–562. [Google Scholar] [CrossRef]
- Tavoosi, J.; Mohammadzadeh, A.; Pahlevanzadeh, B.; Kasmani, M.B.; Band, S.S.; Safdar, R.; Mosavi, A.H. A machine learning approach for active/reactive power control of grid-connected doubly-fed induction generators. Ain Shams Eng. J. 2021, 13, 101564. [Google Scholar] [CrossRef]
- Ghalandari, M.; Shamshirband, S.; Mosavi, A.; Chau, K.-W. Flutter speed estimation using presented differential quadrature method formulation. Eng. Appl. Comput. Fluid Mech. 2019, 13, 804–810. [Google Scholar] [CrossRef] [Green Version]
- Li, Z.; Bouscasse, B.; Ducrozet, G.; Gentaz, L.; Le Touzé, D.; Ferrant, P. Spectral wave explicit navier-stokes equations for wave-structure interactions using two-phase computational fluid dynamics solvers. Ocean. Eng. 2021, 221, 108513. [Google Scholar] [CrossRef]
- Zhou, Y. Ocean energy applications for coastal communities with artificial intelligencea state-of-the-art review. Energy AI 2022, 10, 100189. [Google Scholar] [CrossRef]
- Miskati, S.; Farin, F.M. Performance Evaluation of Wave-Carpet in Wave Energy Extraction at Different Coastal Regions: An Analytical Approach. Doctoral Dissertation, Department of Mechanical and Production Engineering, Calgary, AB, Canada, 2021. [Google Scholar]
- Gu, C.; Li, H. Review on Deep Learning Research and Applications in Wind and Wave Energy. Energies 2022, 15, 1510. [Google Scholar] [CrossRef]
- Aazami, R.; Heydari, O.; Tavoosi, J.; Shirkhani, M.; Mohammadzadeh, A.; Mosavi, A. Optimal Control of an Energy-Storage System in a Microgrid for Reducing Wind-Power Fluctuations. Sustainability 2022, 14, 6183. [Google Scholar] [CrossRef]
- Kabir, M.; Chowdhury, M.; Sultana, N.; Jamal, M.; Techato, K. Ocean renewable energy and its prospect for developing economies. In Renewable Energy and Sustainability; Elsevier: Amsterdam, The Netherlands, 2022; pp. 263–298. [Google Scholar]
- Babajani, A.; Jafari, M.; Hafezisefat, P.; Mirhosseini, M.; Rezania, A.; Rosendahl, L. Parametric study of a wave energy converter (Searaser) for Caspian Sea. Energy Procedia 2018, 147, 334–342. [Google Scholar] [CrossRef]
- He, J. Coherence and cross-spectral density matrix analysis of random wind and wave in deep water. Ocean Eng. 2020, 197, 106930. [Google Scholar] [CrossRef]
- Ijadi Maghsoodi, A. Renewable energy technology selection problem using integrated h-swara-multimoora approach. Sustainability 2018, 10, 4481. [Google Scholar] [CrossRef] [Green Version]
- Band, S.S.; Ardabili, S.; Sookhak, M.; Chronopoulos, A.T.; Elnaffar, S.; Moslehpour, M.; Csaba, M.; Torok, B.; Pai, H.-T.; Mosavi, A. When Smart Cities Get Smarter via Machine Learning: An In-Depth Literature Review. IEEE Access 2022, 10, 60985–61015. [Google Scholar] [CrossRef]
- Shamshirband, S.; Mosavi, A.; Rabczuk, T.; Nabipour, N.; Chau, K.-W. Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines. Eng. Appl. Comput. Fluid Mech. 2020, 14, 805–817. [Google Scholar] [CrossRef]
- Liu, Z.; Mohammadzadeh, A.; Turabieh, H.; Mafarja, M.; Band, S.S.; Mosavi, A. A New Online Learned Interval Type-3 Fuzzy Control System for Solar Energy Management Systems. IEEE Access 2021, 9, 10498–10508. [Google Scholar] [CrossRef]
- Bavili, R.E.; Mohammadzadeh, A.; Tavoosi, J.; Mobayen, S.; Assawinchaichote, W.; Asad, J.H.; Mosavi, A.H. A New Active Fault Tolerant Control System: Predictive Online Fault Estimation. IEEE Access 2021, 9, 118461–118471. [Google Scholar] [CrossRef]
- Akbari, E.; Teimouri, A.R.; Saki, M.; Rezaei, M.A.; Hu, J.; Band, S.S.; Pai, H.-T.; Mosavi, A.H. A Fault-Tolerant Cascaded Switched-Capacitor Multilevel Inverter for Domestic Applications in Smart Grids. IEEE Access 2022, 10, 110590–110602. [Google Scholar] [CrossRef]
- Band, S.S.; Ardabili, S.; Mosavi, A.; Jun, C.; Khoshkam, H.; Moslehpour, M. Feasibility of soft computing techniques for estimating the long-term mean monthly wind speed. Energy Rep. 2022, 8, 638–648. [Google Scholar] [CrossRef]
- Dehghan Manshadi, M.; Ghassemi, M.; Mousavi, S.M.; Mosavi, A.H.; Kovacs, L. Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory. Energies 2021, 14, 4867. [Google Scholar]
- Ponnusamy, V.K.; Kasinathan, P.; Elavarasan, R.M.; Ramanathan, V.; Anandan, R.K.; Subramaniam, U.; Ghosh, A.; Hossain, E. A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid. Sustainability 2021, 13, 13322. [Google Scholar] [CrossRef]
- Ahmad, T.; Zhang, D.; Huang, C.; Zhang, H.; Dai, N.; Song, Y.; Chen, H. Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. J. Clean. Prod. 2021, 289, 125834. [Google Scholar] [CrossRef]
- Wang, G.; Chao, Y.; Cao, Y.; Jiang, T.; Han, W.; Chen, Z. A comprehensive review of research works based on evolutionary game theory for sustainable energy development. Energy Rep. 2021, 8, 114–136. [Google Scholar] [CrossRef]
- Iranmehr, H.; Aazami, R.; Tavoosi, J.; Shirkhani, M.; Azizi, A.-R.; Mohammadzadeh, A.; Mosavi, A.H.; Guo, W. Modeling the Price of Emergency Power Transmission Lines in the Reserve Market Due to the Influence of Renewable Energies. Front. Energy Res. 2022, 9, 944. [Google Scholar] [CrossRef]
- Farmanbar, M.; Parham, K.; Arild, Ø.; Rong, C. A widespread review of smart grids towards smart cities. Energies 2019, 12, 4484. [Google Scholar] [CrossRef] [Green Version]
- Quartier, N.; Crespo, A.J.; Domínguez, J.M.; Stratigaki, V.; Troch, P. Efficient response of an onshore Oscillating Water Column Wave Energy Converter using a one-phase SPH model coupled with a multiphysics library. Appl. Ocean Res. 2021, 115, 102856. [Google Scholar] [CrossRef]
- Mahmoodi, K.; Nepomuceno, E.; Razminia, A. Wave excitation force forecasting using neural networks. Energy 2022, 247, 123322. [Google Scholar] [CrossRef]
- Wang, H.; Alattas, K.A.; Mohammadzadeh, A.; Sabzalian, M.H.; Aly, A.A.; Mosavi, A. Comprehensive review of load forecasting with emphasis on intelligent computing approaches. Energy Rep. 2022, 8, 13189–13198. [Google Scholar] [CrossRef]
- Clemente, D.; Rosa-Santos, P.; Taveira-Pinto, F. On the potential synergies and applications of wave energy converters: A review. Renew. Sustain. Energy Rev. 2020, 135, 110162. [Google Scholar] [CrossRef]
- Felix, A.; Hernández-Fontes, J.V.; Lithgow, D.; Mendoza, E.; Posada, G.; Ring, M.; Silva, R. Wave energy in tropical regions: Deployment challenges, environmental and social perspectives. J. Mar. Sci. Eng. 2019, 7, 219. [Google Scholar] [CrossRef] [Green Version]
- Farrok, O.; Ahmed, K.; Tahlil, A.D.; Farah, M.M.; Kiran, M.R.; Islam, R. Electrical Power Generation from the Oceanic Wave for Sustainable Advancement in Renewable Energy Technologies. Sustainability 2020, 12, 2178. [Google Scholar] [CrossRef] [Green Version]
- Guo, B.; Ringwood, J.V. A review of wave energy technology from a research and commercial perspective. IET Renew. Power Gener. 2021, 15, 3065–3090. [Google Scholar] [CrossRef]
- López-Ruiz, A.; Bergillos, R.J.; Lira-Loarca, A.; Ortega-Sánchez, M. A methodology for the long-term simulation and uncertainty analysis of the operational lifetime performance of wave energy converter arrays. Energy 2018, 153, 126–135. [Google Scholar] [CrossRef]
- Safarian, S.; Saryazdi, S.M.E.; Unnthorsson, R.; Richter, C. Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant. Energy 2020, 213, 118800. [Google Scholar] [CrossRef]
- Kushwah, S. An Oscillating Water Column (OWC): The Wave Energy Converter. J. Inst. Eng. India Ser. C 2021, 102, 1311–1317. [Google Scholar] [CrossRef]
- Pap, J.; Mako, C.; Illessy, M.; Kis, N.; Mosavi, A. Modeling Organizational Performance with Machine Learning. J. Open Innov. Technol. Mark. Complex. 2022, 8, 177. [Google Scholar] [CrossRef]
- Pap, J.; Mako, C.; Illessy, M.; Dedaj, Z.; Ardabili, S.; Torok, B.; Mosavi, A. Correlation Analysis of Factors Affecting Firm Performance and Employees Wellbeing: Application of Advanced Machine Learning Analysis. Algorithms 2022, 15, 300. [Google Scholar] [CrossRef]
- Alanazi, A.; Alanazi, M.; Memon, Z.A.; Mosavi, A. Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method. Appl. Sci. 2022, 12, 7959. [Google Scholar] [CrossRef]
- Shakibjoo, A.D.; Moradzadeh, M.; Din, S.U.; Mohammadzadeh, A.; Mosavi, A.H.; Vandevelde, L. Optimized Type-2 Fuzzy Frequency Control for Multi-Area Power Systems. IEEE Access 2021, 10, 6989–7002. [Google Scholar] [CrossRef]
- Zhang, G.; Band, S.S.; Jun, C.; Bateni, S.M.; Chuang, H.-M.; Turabieh, H.; Mafarja, M.; Mosavi, A.; Moslehpour, M. Solar radiation estimation in different climates with meteorological variables using Bayesian model averaging and new soft computing models. Energy Rep. 2021, 7, 8973–8996. [Google Scholar] [CrossRef]
- Cao, Y.; Raise, A.; Mohammadzadeh, A.; Rathinasamy, S.; Band, S.S.; Mosavi, A. Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction. Energy Rep. 2021, 7, 8115–8127. [Google Scholar] [CrossRef]
- Tavoosi, J.; Suratgar, A.; Menhaj, M.; Mosavi, A.; Mohammadzadeh, A.; Ranjbar, E. Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction. Sustainability 2021, 13, 3301. [Google Scholar] [CrossRef]
- Bourouis, S.; Band, S.S. Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images. Front. Oncol. 2022, 12, 834028. [Google Scholar] [CrossRef]
- Mosavi, A.H.; Mohammadzadeh, A.; Rathinasamy, S.; Zhang, C.; Reuter, U.; Levente, K.; Adeli, H. Deep learning fuzzy immersion and invariance control for type-I diabetes. Comput. Biol. Med. 2022, 149, 105975. [Google Scholar] [CrossRef] [PubMed]
- Almutairi, K.; Algarni, S.; Alqahtani, T.; Moayedi, H.; Mosavi, A. A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings. Sustainability 2022, 14, 5924. [Google Scholar] [CrossRef]
- Ahmad, Z.; Zhong, H.; Mosavi, A.; Sadiq, M.; Saleem, H.; Khalid, A.; Mahmood, S.; Nabipour, N. Machine Learning Modeling of Aerobic Biodegradation for Azo Dyes and Hexavalent Chromium. Mathematics 2020, 8, 913. [Google Scholar] [CrossRef]
- Mosavi, A.; Shokri, M.; Mansor, Z.; Qasem, S.N.; Band, S.S.; Mohammadzadeh, A. Machine Learning for Modeling the Singular Multi-Pantograph Equations. Entropy 2020, 22, 1041. [Google Scholar] [CrossRef] [PubMed]
- Ardabili, S.; Mosavi, A.; Dehghani, M.; Várkonyi-Kóczy, A.R. Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review. In Proceedings of the International Conference on Global Research and Education, Balatonfüred, Hungary, 4–7 September 2019; pp. 52–62. [Google Scholar]
- Moayedi, H.; Mosavi, A. Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings. Energies 2021, 14, 1649. [Google Scholar] [CrossRef]
- Rezakazemi, M.; Mosavi, A.; Shirazian, S. ANFIS pattern for molecular membranes separation optimization. J. Mol. Liq. 2019, 274, 470–476. [Google Scholar] [CrossRef]
- Mosavi, A.; Faghan, Y.; Ghamisi, P.; Duan, P.; Ardabili, S.; Salwana, E.; Band, S. Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics. Mathematics 2020, 8, 1640. [Google Scholar] [CrossRef]
- Samadianfard, S.; Jarhan, S.; Salwana, E.; Mosavi, A.; Shamshirband, S.; Akib, S. Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin. Water 2019, 11, 1934. [Google Scholar] [CrossRef] [Green Version]
- Moayedi, H.; Mosavi, A. Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings. Energies 2021, 14, 1331. [Google Scholar] [CrossRef]
- Choubin, B.; Mosavi, A.; Alamdarloo, E.H.; Hosseini, F.S.; Shamshirband, S.; Dashtekian, K.; Ghamisi, P. Earth fissure hazard prediction using machine learning models. Environ. Res. 2019, 179, 108770. [Google Scholar] [CrossRef] [PubMed]
- Mohammadzadeh, S.D.; Kazemi, S.F. Prediction of compression index of fine-grained soils using a gene expression programming model. Infrastructures 2019, 4, 26. [Google Scholar] [CrossRef] [Green Version]
- Karballaeezadeh, N.; Mohammadzadeh, S.D.; Shamshirband, S.; Hajikhodaverdikhan, P. Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road). Eng. Appl. Comput. Fluid Mech. 2019, 13, 188–198. [Google Scholar] [CrossRef] [Green Version]
- Rezaei, M.A.; Fathollahi, A.; Rezaei, S.; Hu, J.; Gheisarnejad, M.; Teimouri, A.R.; Rituraj, R.; Mosavi, A.; Khooban, M.-H. Adaptation of A Real-Time Deep Learning Approach with An Analog Fault Detection Technique for Reliability Forecasting of Capacitor Banks Used in Mobile Vehicles. IEEE Access 2022, 21, 89–99. [Google Scholar] [CrossRef]
- Khakian, R.; Karimimoshaver, M.; Aram, F.; Benis, S.Z.; Mosavi, A.; Varkonyi-Koczy, A.R. Modeling Nearly Zero Energy Buildings for Sustainable Development in Rural Areas. Energies 2020, 13, 2593. [Google Scholar] [CrossRef]
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Mirshafiee, F.; Shahbazi, E.; Safi, M.; Rituraj, R. Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study. Energies 2023, 16, 502. https://doi.org/10.3390/en16010502
Mirshafiee F, Shahbazi E, Safi M, Rituraj R. Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study. Energies. 2023; 16(1):502. https://doi.org/10.3390/en16010502
Chicago/Turabian StyleMirshafiee, Fatemehsadat, Emad Shahbazi, Mohadeseh Safi, and Rituraj Rituraj. 2023. "Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study" Energies 16, no. 1: 502. https://doi.org/10.3390/en16010502
APA StyleMirshafiee, F., Shahbazi, E., Safi, M., & Rituraj, R. (2023). Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study. Energies, 16(1), 502. https://doi.org/10.3390/en16010502