Hüseyin ALTINKAYA, Mustafa YILMAZ

34214

Estimation of the Daily Production Levels of a Run-of-River Hydropower Plant Using the Artificial Neural Network

Estimation of the Daily Production Levels of a Run-of-River Hydropower Plant Using the Artificial Neural Network

Renewable energy sources, as well as the studies being conducted regarding these energy sources, are becoming increasingly important for our world. In this manuscript, the daily energy production level of a small (15 MW) run-of-river hydropower plant (RRHPP) was estimated using the artificial neural network (ANN) model. In this context, the model utilized both meteorological data and HPP-related data. The input parameters of the artificial neural network included the daily total precipitation, daily mean temperature, daily mean water vapour pressure, daily mean relative humidity, and the daily mean river water elevation at the hydropower plant, while the only output parameter consisted of the total daily energy production. For the ANN, data from the four years between 2017 and 2020 were used for training purposes, while data from the first eight months of 2021 were used for testing purposes. Ten different ANN networks were tested. A comparison of the ANN data with the real data indicated that the model provided satisfying results. The minimum error rate was 0.13%, the maximum error rate was 9.13%, and the mean error rate was 3.13%. Furthermore, six different algorithms were compared with each other. It was observed that the best results were obtained from the Levenberg-Marquardt algorithm.This study demonstrated that the ANN can estimate the daily energy production of a run-of-river HPP with high accuracy and that this model can potentially contribute to studies investigating the potential of renewable energies.
Keywords:

Artificial neural network run-of-river hydropower plant, renewable energy,

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  • WED, World Energy Data, https://www.worldenergydata.org/world-electricity-generation/
  • TEİAŞ, Turkish Electricity Transmission Corporation, https://www.teias.gov.tr/tr-TR/aylik-elektrik-uretim-tuketim-raporlari
  • TEİAŞ, Turkish Electricity Transmission Corporation, https://www.teias.gov.tr/tr-TR/kurulu-guc-raporlari
  • Fereidoon M., Najimi M., Khorasani, G., Simulation of hydropower systems operation using artificial neural network, International Journal of Emerging Science and Engineering, Volume-1, Issue-12, (2013) 86-89.
  • Yadav D., Sharma V., Artificial neural network based hydroelectric generation modelling. International Journal of Applied Engineering Research, Volume-1, No-3, (2010) 343-359.
  • Kuriqi A., Antonio NP., Ward AS., Garrote L., Flow regime aspects in determining environmental flows and maximising energy production at run-of-river hydropower plants, Applied Energy, 256 (2019) 113980.
  • Brito MA., Rodriguez DA., Junior VLC., Vianna JNS., The climate change potential effects on the run-of-river plant and the environmental and economic dimensions of sustainability, Renewable and Sustainable Energy Reviews, 147 (2021) 111238 1-21.
  • Gerini F., Vagnoni E., Cherkaoui R., Paolone M., Improving frequency containment reserve provision in run-of-river hydropower plants, Sustainable Energy, Grids and Networks, 28 (2021) 100538 1-12.
  • Csiki, S.J.C., & Rhoads B.L., Influence of four run-of-river dams on channel morphology sediment characteristics in Illinois, USA. Geomorphology, 206, (2014) 215–229.
  • Nwobi-Okoyea, C.C., & Igboanugob, A.C., (2013). Predicting water levels at Kainji dam using artificial neural network. Nigerian Journal of Technology, 32, 129-136.
  • Sharma, H., Singh, J., Run off river plant: Status and prospects, International Journal of Innovative Technology and Exploring Engineering, Volume-3, Issue-2, (2013) 210-213.
  • Özdemir MS., Dalcalı A., Ocak C., Run of river Hydroelectric Power Plants and Turbine-Generators Used in These Power Plants, Müh. Bil. Ve Araş. Dergisi 2(2) (2020) 69-75.
  • Anagnostopoulos, J.S., Papantonis D.E., Optimal sizing of a run-of-river small hydropower plant, Energy Conversion and Management, 48, (2007) 2663–2670.
  • Mishra, S., Singal, S.K. & Khatod D.K., Optimal installation of small hydropower plant—A review, Renewable and Sustainable Energy Reviews, 15, (2011) 3862-3869.
  • Singal, S.K., Saini, R.P., Raghuvanshi C.S., Analysis for cost estimation of low head run-of-river small hydropower schemes, Energy for Sustainable Development, 14, (2010) 117-126.
  • Ardizzon, G., Cavazzini, G. & Pavesi, G., A new generation of small hydro and pumped-hydro power plants: Advances and future challenges, Renewable and Sustainable Energy Reviews, 31, (2014) 746-761.
  • Kumar, D. & Katoch, S.S., Sustainability indicators for run of the river (RoR) hydropower projects in hydro rich regions of India, Renewable and Sustainable Energy Reviews, 35, (2014) 101-108.
  • Molina J.M., Isasi, P., Berlanga, A. & Sanchis A., Hydroelectric power plant management relying on neural networks and expert system integration, Engineering Applications of Artifcial Intelligence, 13, (2000) 357-369.
  • Dumur, D., Libaux A. & Boucher, P., Robust control for a Basse-Isere run-of-river cascaded hydro-electric plants. Proceedings of the 2001 IEEE International Conference on Control Applications September 5-7, Mexico City, Mexico, (2001). 1083-1088.
  • Kishor, N., Nonlinear predictive control to track deviated power of an identified NNARX model of a hydro plant, Expert Systems with Applications, 35 (2008)1741–1751.
  • Pérez-Díaz, J.I., Fraile-Ardanuy J., Neural networks for optimal operation of a run-of-river adjustable speed hydro power plant with axial-flow propeller türbine, 16th Mediterranean Conference on Control and Automation Congress Centre, Ajaccio, France, 25-27 June, (2008) 309-314
  • Salhi, I., Doubabi, S., Essounbouli, N. & Hamzaoui, A., Frequency regulation for large load variations on micro-hydro power plants with real-time implementation. International Journal of Electrical Power & Energy Systems, 60 (2014) 6-13.
  • İnallı, K., Işık, E., Dağtekin, İ., The prediction of efficiency and production parameters in Karakaya hpp using the artificial network. Dicle University Engineering Faculty Journal, 5 (2014) 59-68.
  • Beale, M. H., Hagan, M. T., & Demuth, H. B. (2013). Neural network toolbox user’s guide. MathWorks, Technical Support.
  • Nabiyev V.V. (2005). Artificial Intelligence. Seckin publishing.
  • Öztemel E. (2003). Artificial Neural Networks. Seckin publishing.
  • https://www.mathworks.com/help/deeplearning/ug/multilayer-neural-network-architecture.html
  • Altınkaya, H., Orak İ.M. &Esen İ., Artificial neural network application for modeling the rail rolling process, Expert Systems with Applications, 41 (2014) 7135–7146.
  • Canakci, M., Ozsezen, A.N., Arcaklioglu, E., & Erdil, APrediction of performance and exhaust emissions of a diesel engine fuelled with biodiesel produced from waste frying palm oil, Expert Systems with Applications, 36 (2009) 9268-9280.
  • Koca, A., Oztop, H.F., Varol, Y., & Koca, G.O.,Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey, Expert Systems with Applications, 38 (2011) 8756-8762.
  • Ozgoren, M., Bilgili, M., & Sahin, B., Estimation of global solar radiation using ANN over Turkey. Expert Systems with Applications, 39 (2012) 5043-5051.
  • Ekinci F., YSA ve ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 7 (2019) 1029-1044.
  • Gündoğdu A., Çelikel R., ANN-Based MPPT Algorithm for Photovoltaic Systems, Turkish Journal of Science & Technology 15(2), (2020)101-110.
  • Kocaarslan İ., Akcay MT., Akgundoğdu A., Tiryaki H., Comparison of ANN and ANFIS Methods for the Voltage-Drop Prediction on an Electric Railway Line, Electrica (2018); 18(1): 26-35.
  • Işık H., Şeker M., Yapay Sinir Ağı (YSA) Kullanarak Farklı Kaynaklardan Türkiye’de Elektrik Enerjisi Üretim Potansiyelinin Tahmini, Journal of Computer Science. (2021) pp. 304-311, http://doi.org/10.53070/bbd.991039
  • Uzlu E., Estimates of hydroelectric energy generation in Turkey with Jaya algorithm-optimized artificial neural networks, GU J Sci, Part C, 9(3): 446-462 (2021).
  • Mustafa Şeker. Yapay Sinir Ağı (YSA) Kullanılarak Meteorolojik Verilere Dayalı Solar Radyasyon Tahmini, DEÜ FMD 23(69), 923-935, (2021).
  • Ilaboya, I.R and Igbinedion O. E. Performance of Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) for the Prediction of Monthly Maximum Rainfall in Benin City, Nigeria, International Journal of Engineering Science and Application, Vol. 3, No. 1, (2019).
  • Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River (1999).
  • Eker, E., Kayri, M., Ekinci, S., İzci, D., A New Fusion of ASO with SA Algorithm and Its Applications to MLP Training and DC Motor Speed Control, Arabian Journal for Science and Engineering, 46, (2021) 3889–3911.
  • Gülcü, Ş., Training of the feed forward artificial neural networks using dragonfly algorithm, Applied Soft Computing, Applied Soft Computing, 124, (2022) 109023.
  • Kulluk, S., Ozbakir, L., Baykasoglu, A., Training neural networks with harmony search algorithms for classification problems, Eng. Appl. Artif. Intell. 25 (2012) 11–19.
  • Valian, E., Mohanna, S., Tavakoli, S., Improved cuckoo search algorithm for feedforward neural network training, Int. J. Artif. Intell. Appl. 2 (2011) 36–43.
  • Faris, H., Aljarah, I., Mirjalili, S., Improved monarch butterfly optimization for unconstrained global search and neural network training, Appl. Intell. 48 (2018) 445–464.
  • Erdoğan, F., Gülcü, S., Training of artificial neural networks using meta heuristic algorithms, in: The International Aluminium-Themed Engineering and Natural Sciences Conference (IATENS19), Konya, Turkey, (2019), 124–128.
  • Xu, J., Yan, F., Hybrid Nelder–Mead algorithm and dragonfly algorithm for function optimization and the training of a multilayer perceptron. Arab. J. Sci. Eng. 44, (2019) 3473–3487.
  • Mirjalili, S., How effective is the grey wolf optimizer in training multi-layer perceptrons, Appl. Intell. 43 (2015) 150–161.
  • Zhang, X., Wang, X., Chen, H., Wang, D., Fu, Z., Improved GWO for large-scale function optimization and MLP optimization in cancer identification. Neural Comput. Appl. 32, (2020) 1305– 1325.
  • Tang, R., Fong, S., Deb, S., Vasilakos, A.V., Millham, R.C., Dynamic group optimisation algorithm for training feed-forward neural networks, Neurocomputing 314 (2018) 1–19.
  • Heidari, A.A.; Faris, H.; Mirjalili, S.; Aljarah, I.; Mafarja, M.: Ant lion optimizer: theory, literature review, and application in multi-layer perceptron neural networks. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds.) Studies in computational intelligence, pp. 23–46. Springer International Publishing, Cham (2020).
  • Khishe, M.; Mosavi, M.R., Classification of underwater acoustical dataset using neural network trained by Chimp Optimization Algorithm. Appl. Acoust. 157, (2020) 107005.
  • Heidari, A.A., Faris, H., Aljarah, I., Mirjalili, S., An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft. Comput. 23, (2019) 7941–7958.
  • Khishe, M., Mohammadi, H., Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm. Ocean Eng. 181, (2019) 98–108.
  • Ghanem, W.A.H.M., Jantan, A., Training a neural network for cyberattack classification applications using hybridization of an artificial bee colony and monarch butterfly optimization. Neural Process. Lett. 51, (2020) 905–946.
  • Huang, G. B., Babri, H. A., Upper bounds on the number of hidden neurons in feed forward networks with arbitrary bounded nonlinear activation functions. IEEE Transactions on Neural Networks, 9(1) (1998).
  • Huang, S. C., Huang, Y. F. Bounds on the number of hidden neurons in multilayer perceptrons, IEEE Transactions on Neural Networks, 2(1), (1991) 47–55.
  • Karsoliya, S., Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 3(6), 714–717. ISSN: 2231-5381 (2012).
  • Khaw, J. F. C., Lim, B. S., & Lim, L. E. N., Optimal design of neural networks using the Taguchi method. Neurocomputing, 7 (1994) 225–245.
  • Panchal, G., Ganatra, A., Kosta, Y. P., & Panchal, D., Behaviour Analysis of Multilayer Perceptronswith Multiple Hidden Neurons and Hidden Layers, International Journal of Computer Theory and Engineering 3(2). ISSN: 1793-8201 (2011).
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  • Yayın Aralığı: Yılda 3 Sayı
  • Başlangıç: 2022
  • Yayıncı: Akademik Perspektif Derneği
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