Renewable Energy Resources and Artificial Intelligence Applications

Aksu, H. H., Kumaş, K., İnan, O., Akyüz, A., & Güngör, A. (2019). Determination and spatial analysis of Muğla, Türkiye biogas potential by tier methodologies. AIP Conference Proceedin- gs 2178,1. https://doi.org/10.1063/1.5135442

Kumaş,K., İnan,O., & Akyüz, A.(2022). The Evolution of the Biogas Potential of the Lakes Re- gion, Türkiye by Years: Comparison at Provincial Level. J. Int. Environmental Application & Science, 17(3),115-122.

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İnan, O., Kumaş, K., Akyüz, A., & Güngör, A. (2019). A Robust Fuzzy Model For Wind Energy Potential Estimation. In A.Günday, E. Pehlivan, M. R. Minaz (Eds.), Engineering Research Papers (chapter 18, pp. 305-315). Ankara:Gece Publishing.

Sevdim, R. E. (2018). Hybrid of renewable energy resources modeling usage with structural intelligence. (Master’s thesis). DSpace@Gelişim.

Külekçi, Ö. C. (2009). Place of geothermal energy in the content of renewable energy sources and it’ s importance for Türkiye. Ankara University Journal of Environmental Sciences, 1(2), 83-91.

Bütüner, M. A., & Koşalay, İ. (2022, August). Machine learning based anomaly detection in hydraulic units of governors in hydroelectric power plants. In The 14th International Scientific Research Congress (p. 55).

Özbay, E., & Gençoğlu, M. T. (2009, June). Modeling of hydroelectric power plants. V. Re- newable Energy Resources Symposium (p.108).

Toklu, E. (2017). Biobass energy potential and utilzation in Türkiye. Renewable Energy, 107, 235-244.

Kumaş, K. & Akyüz, A. Ö. (2021). Biogas Potential, CO2 Emission and Electrical Energy Equi- valent from Animal Waste in Burdur/Türkiye. Academia Journal of Nature and Human Scien- ces, 7(1), 52-62.

Ayan, A. (2022). Evaluation of usage opportunities of biomass energy in Türkiye. (Master’s thesis). DSpace@Maltepe.

Özkan, F. (2019). Techno-economic analysis of severse osmosis seawater desalination systems powered by renewable energy. (Master›s thesis).

Örer, G., Gürsel, K.T., Özdamar, A., Özbalta, N. (2003). Overview of wave energy facilities. 2nd Renewable Energy Sources Symposium (p.126-140).

Ürün, E., & Soyu, E. (2016). A review of the renewable energy resources in the energy producti- on of Türkiye. Dumlupınar University, Journal of Social Sciences, ICEBSS Special Issue, 31-45.

Nabiyev, V. V. (2012). Artificial intelligence. Ankara: Seçkin Press.

Metlek, S. & Çetiner, H. (2021). Deep learning applications in matlab environment. Ankara: Iksad Press.

Goodfellow, I., Bengio, Y., Courville, A. (2018). Deep learning. Ankara: Buzdağı Press.

Han, J. & Kamber, M. (2006). Data mining: concepts and techniques. San Francisco: Morgan Kaufmann.

İşçi, Ö. & Korukoğlu, S. (2003). Genetic algorithm approach and an application in operations research. Management and Economics. Journal of Celal Bayar University Faculty of Economics and Administrative Sciences, 10(2), 191-208.

Yen, J. & Langari, R. (1998). Fuzzy Logic: Intelligence, Control, and Information. New Jersey: Prentice Hall.

Tiryaki, A. E., & Kazan, R. (2007). Modeling the dishwasher with fuzzy logic. Engineer and Machine, 48(565), 3-8.

Dülger, Ö. (2014). Predicting mathematics 1 course success by using hierarchical adaptive network based fuzzy inference system. Pamukkale University Journal of Engineering Sciences, 20(5), 166-173.

Erdoğmuş, P. (2016). Nature inspired optimization algorithms and optimization of the optimi- zation algorithms. Düzce University Journal of Science and Technology, 4(1), 293-304.

Çelik, U., Yurtay, N., & Yurtay, Y. (2009, June). Pattern recognition in biotechnology. 29. Nati- onal Congress of Operations Research and Industrial Engineering.

Coşkun, C., Koçyiğit, N., & Oktay, Z. (2016). Estimation of pv modüle surface temperature using artifıcial neural networks. Muğla Journal of Science and Technology, 2(2), 15-18.

Ayan, S. M. İ., (2018). Power estimation of photovoltaic system with the artificial intelligence algorithm. (Master’s thesis). DSpace@Kırklareli.

Demirtaş, M., Akkoyun, N. , Akkoyun, E. & Çetinbaş, İ. (2019). The probabilistic prediction of solar energy power production based on time in smart grids. Gazi University Journal of Scien- ce Part C: Design and Technology, 7(2), 411-424.

Li, J., Ward, J., Tong, J., Collins, L., & Platt, G. (2016). Machine learning for solar irradiance forecasting of photovoltaic system. Renewable Energy, 90, 542-553.

Meenal, R. & Selvakumar, A.I. (2018). Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters. Renewable Energy, 121, 324–343.

Kara, A. (2019). Global solar irradiance time series prediction using long short-term memory network. Gazi University Journal of Science Part C: Design and Technology, 7(4), 882-892.

Yagli, G. M., Yang, D., & Srinivasan, D. (2019). Automatic hourly solar forecasting using mac- hine learning models. Renewable and Sustainable Energy Reviews, 105, 487-498.

Al-Rousan, N., Al-Najjar, H., & Alomari, O. (2021). Assessment of predicting hourly global solar radiation in Jordan based on rules, trees, meta, lazy and function prediction methods. Sustainable Energy Technologies and Assessments, 44, 100923.

Zhu, H. L., Li, X., Sun, Q., Nie, L., Yao, J. X., Zhao, G. (2016). A power prediction method for photovoltaic power plant based on wavelet decomposition and artificial neural networks. Energies, 9(1), 11.

Leva, S., Dolara, A., Grimaccia, F., Mussetta, M., Ogliari, E. (2017). Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power. Mathematics and Computers in Simulation, 131, 88-100.

Dandıl, E. & Gürgen, E., 2019. Prediction of photovoltaic panel power outputs using artificial neural networks and comparison with heuristic algorithms. European Journal of Science and Technology, 16, 146-158.

Ziane, A., Necaibia, A., Sahouane, N., Dabou, R., Mostefaoui, M., Bouraiou, A., Khelifi, S., Rouabhia, A., Blal, M. (2021). Photovoltaic output power performance assessment and fore- casting: Impact of meteorological variables. Solar Energy, 220, 745-757.

Tümay Ateş, K. (2022). Short term photovoltaic power plant output power forecasting with hybrid method developed using multilayer artificial neural network model and cultural algo- rithm model. Osmaniye Korkut Ata University Journal of the Institute of Science and Techno- logy, 5(1), 342-354.

Akal, D. & Umut, İ. (2022). Using Artificial Intelligence Methods for Power Estimation in Pho- tovoltaic Panels. Journal of Tekirdag Agricultural Faculty, 19(2), 435-445.

Liu, H., Mi, X., & Li, Y. (2018). Smart multi-step deep learning model for wind speed forecas- ting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM. Energy Conversion and Management, 159, 54-64.

Maroufpoor, S., Sanikhani, H., Kisi, O., Deo, R. C. & Yaseen, Z. M. (2019). Long-term model- ling of wind speeds using six different heuristic artificial intelligence approaches. International Journal of Climatology, 39(8), 3543-3557.

Nezhad, M. M., Heydari, A., Pirshayan, E., Groppi, D., & Garcia, D. A. (2021). A novel fore- casting model for wind speed assessment using sentinel family satellites images and machine learning method. Renewable Energy, 179, 2198-2211.

Hur, S. H. (2021). Short-term wind speed prediction using Extended Kalman filter and machi- ne learning. Energy Reports, 7, 1046-1054.

Lin, B., & Zhang, C. (2021). A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China. Renewable Energy, 179, 1565-1577.

Doğancı, Ö. , Ertürk, M. , Özsunar, A. & Arcaklıoğlu, E. (2016). A study of the estimation of the wind energy in the central-western Black Sea region. Journal of Advanced Technology Sciences, 5(1), 153-163.

Wang, H. Z., Li, G. Q., Wang, G. B., Peng, J. C., Jiang, H., & Liu, Y. T. (2017). Deep learning based ensemble approach for probabilistic wind power forecasting. Applied energy, 188, 56-70.

Yu, R., Gao, J., Yu, M., Lu, W., Xu, T., Zhao, M., Zhang, J., Zhang, R., Zhang, Zh. (2019). LSTM- EFG for wind power forecasting based on sequential correlation features. Future Generation Computer Systems, 93, 33-42.

Qin, Y., Li, K., Liang, Zh., Lee, B., Zhang, F., Gu, Y., Zhang, L., Wu, F., Rodriguez, D. (2019). Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal. Applied Energy, 236, 262-272.

Lahouar, A. & Slama, B. H. J. (2017). Hour-ahead wind power forecast based on random fo- rests. Renewable Energy, 109, 529-541.

Sambana, B., Appala Naidu, P., Jarabala, R., & Narayana, V. N. S. L. (2021). Performance vali- dation of wind turbines using machine learning methodologies. Materials Today: Proceedings.

Moraga, J., Duzgun, H. S., Cavur, M., & Soydan, H. (2022). The Geothermal Artificial Intelli- gence for geothermal exploration. Renewable Energy, 192, 134-149.

Shibli, M. A., & Mathew, B. (2019). Artificial intelligent machine learning and big data mining of desert geothermal heat pump: analysis, design and control. International Journal of Intelli- gent Systems and Applications, 11(4), 1-13.

Åsnes, A., Willersrud, A., & Imsland, L. (2018, October). Predictive maintenance and life cycle estimation for hydro power plants with real-time analytics. Hydro Conference.

Welte, T., & Foros, J. (2019). Monitor X Digitalization in Hydropower. REPORT 2019:618, Energiforsk.

İnal, S. (2022). Operation of hydroelectric power plants dam reservoirs with artificial intelli- gence. (Master’s thesis). DSpace@ODÜ.

Beltramo, T., Ranzan, C., Hinrichs, J., & Hitzmann, B. (2016). Artificial neural network predi- ction of the biogas flow rate optimised with an ant colony algorithm. Biosystems Engineering, 143, 68-78.

Zareei, S., & Khodaei, J. (2017). Modeling and optimization of biogas production from cow manure and maize straw using an adaptive neuro-fuzzy inference system. Renewable Energy, 114, 423-427.

Ghatak, M. D., & Ghatak, A. (2018). Artificial neural network model to predict behavior of biogas production curve from mixed lignocellulosic co-substrates. Fuel, 232, 178-189.

De Clercq, D., Jalota, D., Shang, R., Ni, K., Zhang, Z., Khan, A., ... & Yuan, K. (2019). Machine learning powered software for accurate prediction of biogas production: A case study on in- dustrial-scale Chinese production data. Journal of Cleaner Production, 218, 390-399.

Fernández, J. C., Salcedo-Sanz, S., Gutiérrez, P. A., Alexandre, E., & Hervás-Martínez, C. (2015). Significant wave height and energy flux range forecast with machine learning classi- fiers. Engineering Applications of Artificial Intelligence, 43, 44-53.

Ghosh, S., Chakraborty, T., Saha, S., Majumder, M., & Pal, M. (2016). Development of the loca- tion suitability index for wave energy production by ANN and MCDM techniques. Renewable and Sustainable Energy Reviews, 59, 1017-1028.

James, S. C., Zhang, Y., & O’Donncha, F. (2018). A machine learning framework to forecast wave conditions. Coastal Engineering, 137, 1-10.

Li, L., Yuan, Z., & Gao, Y. (2018). Maximization of energy absorption for a wave energy conver- ter using the deep machine learning. Energy, 165, 340-349.

Ahmadi, S. & Abdi, S. (2016). Application of the hybrid big bang-big crunch algorithm for optimal sizing of a stand-alone hybrid PV/wind/battery system. Solar Energy, 134, 366-374.

Fathy, A. (2016). A reliable methodology based on mine blast optimization algorithm for op- timal sizing of hybrid PV-wind-FC system for remote area in Egypt. Renewable Energy, 95, 367-380.

Hatata, A. Y., Osman, G. & Aladl, M. M. (2018). An optimization method for sizing a solar/ wind/battery hybrid power system based on the artificial immune system. Sustainable Energy Technologies and Assessments, 27, 83-93.

Khosravi, A., Syri, S., Zhao, X., & Assad, M. E. H. (2019). An artificial intelligence approach for thermodynamic modeling of geothermal based-organic Rankine cycle equipped with solar system. Geothermics, 80, 138-154.

Kaya, Ü. (2019). Off grid hybrid wind-solar energy production system’s energy flow control via fuzzy logic. (Master’s thesis). DSpace@AKÜ.

Moghaddam, H. M. J., Kalam, A., Nowdeh, S. A., Ahmadi, A., Babanezhad, M., & Saha, S. (2019). Optimal sizing and energy management of stand-alone hybrid photovoltaic/wind sys- tem based on hydrogen storage considering LOEE and LOLE reliability indices using flower pollination algorithm. Renewable Energy, 135, 1412-1434.

Ayan, S. (2019). Size optimization for hybrid (photovoltaic-wind) energy aytem using heuristic optimization algoritm. (Master’s thesis). DSpace@Kırklareli.

Demollı, H. (2020). Renewable energy power generation forecasting and optimal hybrid power system design using machine learning techniques (Doctoral thesis).