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Research Article

Impact of climatic factors on the prediction of hydroelectric power generation: a deep CNN-SVR approach

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Article: 2253203 | Received 16 Apr 2023, Accepted 24 Aug 2023, Published online: 13 Sep 2023

References

  • Abdulkadir TS, Salami AW, Anwar AR, Kareem AG. 2013. Modelling of hydropower reservoir variables for energy generation: neural network approach. Ethiop J Env Stud Manag. 6(3):310–316. doi: 10.4314/ejesm.v6i3.12.
  • Barzola-Monteses J, Gómez-Romero J, Espinoza-Andaluz M, Fajardo W. 2022. Hydropower production prediction using artificial neural networks: an Ecuadorian application case. Neural Comput Appl. 34(16):13253–13266. doi: 10.1007/s00521-021-06746-5.
  • Bergstra J, Yamins D, Cox DD. 2013. Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms. In Proceedings of the 12th Python in science conference (13). doi: 10.25080/Majora-8b375195-003.
  • Boadi SA, Owusu K. 2019. Impact of climate change and variability on hydropower in Ghana. Afr Geograph Rev. 38(1):19–31. doi: 10.1080/19376812.2017.1284598.
  • Castangia M, Aliberti A, Bottaccioli L, Macii E, Patti E. 2021. A compound of feature selection techniques to improve solar radiation forecasting. Expert Syst Appl. 178:114979. doi: 10.1016/j.eswa.2021.114979.
  • Cavalli S, Amoretti M. 2021. CNN-based multivariate data analysis for bitcoin trend prediction. Appl Soft Comput. 101:107065. doi: 10.1016/j.asoc.2020.107065.
  • Chen CW, Tsai YH, Chang FR, Lin WC. 2020. Ensemble feature selection in medical datasets: combining filter wrapper and embedded feature selection results. Expert Syst. 37(5):12553. doi: 10.1111/exsy.12553.
  • Cihan P, Kalipsiz O, Gökçe E. 2017. Hayvan Hastaliği Teşhisinde Normalizasyon Tekniklerinin 508 Yapay Sinir Aği Ve Özellik Seçim Performansina Etkisi. Electron Turkish Stud. 12(11): 59–70.
  • Cobaner M, Haktanir T, Kisi O. 2008. Prediction of hydropower energy using ANN for the feasibility of hydropower plant installation to an existing irrigation dam. Water Resour Manage. 22(6):757–774. doi: 10.1007/s11269-007-9190-z.
  • Condemi C, Casillas-Perez D, Mastroeni L, Jiménez-Fernández S, Salcedo-Sanz S. 2021a. Hydro-power production capacity prediction based on machine learning regression techniques. Knowl Based Syst. 222:107012. doi: 10.1016/j.knosys.2021.107012.
  • Condemi C, Mastroeni L, Vellucci P. 2021b. The selection of predictive variables in aggregate hydroelectric generation models. JEM. 14(1). doi: 10.21314/JEM.2020.215.
  • Dehghani M, Riahi-Madvar H, Hooshyaripor F, Mosavi A, Shamshirband S, Zavadskas EK, Chau KW. 2019. Prediction of hydropower generation using Grey Wolf optimization adaptive neuro-fuzzy inference system. Energies. 12(2):289. doi: 10.3390/en12020289.
  • Drakaki KK, Sakki GK, Tsoukalas I, Kossieris P, Efstratiadis A. 2022. Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data. Adv Geosci. 56:155–162. doi: 10.5194/adgeo-56-155-2022.
  • Ehtearm M, Ghayoumi Zadeh H, Seifi A, Fayazi A, Dehghani M. 2023. Predicting hydropower production using deep learning CNN-ANN hybridized with Gaussian process regression and Salp algorithm. Water Resour Manage. 37(9):3671–3697. doi: 10.1007/s11269-023-03521-0.
  • Ekanayake P, Wickramasinghe L, Jayasinghe JJW, Rathnayake U. 2021. Regression-based prediction of power generation at samanalawewa hydropower plant in Sri Lanka using machine learning. Math Prob Eng. 2021:1–12. doi: 10.1155/2021/4913824.
  • Atlası E. 2023. https://www.enerjiatlasi.com/elektrik-uretimi/. [accessed 2023 Feb 4].
  • Fugazza D, Manara V, Senese A, Diolaiuti G, Maugeri M. 2021. Snow cover variability in the greater alpine region in the MODIS era (2000–2019). Remote Sens. 13(15):2945. doi: 10.3390/rs13152945.
  • Gallardo-Saavedra S, Redondo-Plaza A, Fernández-Martínez D, Alonso-Gómez V, Morales-Aragonés J, I, Hernández-Callejo L. 2022. Integration of renewable energies in the urban environment of the city of Soria (Spain). World Develop Sustain. 1:100016. doi: 10.1016/j.wds.2022.100016.
  • Gao B, Huang X, Shi J, Tai Y, Zhang J. 2020. Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks. Renew Energ. 162:1665–1683. doi: 10.1016/j.renene.2020.09.141.
  • García-Hinde O, Terrén-Serrano G, Hombrados-Herrera MÁ, Gómez-Verdejo V, Jiménez-Fernández S, Casanova-Mateo C, Sanz-Justo J, Martínez-Ramón M, Salcedo-Sanz S. 2018. Evaluation of dimensionality reduction methods applied to numerical weather models for solar radiation forecasting. Eng Appl Artif Intell. 69:157–167. doi: 10.1016/j.engappai.2017.12.003.
  • Ghimire S, Bhandari B, Casillas-Perez D, Deo RC, Salcedo-Sanz S. 2022. Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland Australia. Eng Appl Artif Intell. 112:104860. doi: 10.1016/j.engappai.2022.104860.
  • Guo L, Chen J, Wu F, Wang M. 2018. An electric power generation forecasting method using support vector machine. Syst Sci Control Eng. 6(3):191–199. doi: 10.1080/21642583.2018.1544947.
  • Hammid AT, Sulaiman MH, Abdalla AN. 2018. Prediction of small hydropower plant power production in Himreen Lake dam (HLD) using artificial neural network. Alex Eng J. 57(1):211–221. doi: 10.1016/j.aej.2016.12.011.
  • Hanoon MS, Najah Ahmed A, Razzaq A, Oudah AY, Alkhayyat A, Feng Huang Y, Kumar P, El-Shafie A. 2023. Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China. Ain Shams Eng J. 14(4):101919. doi: 10.1016/j.asej.2022.101919.
  • Harvey A, Brown A, Hettiarachi P, Inversin A. 1993. Micro-hydro design manual. Intermediate Technology Publications, UK.
  • Izquierdo-Monge O, Peña-Carro P, Villafafila-Robles R, Duque-Perez O, Zorita-Lamadrid A, Hernandez-Callejo L. 2021. Conversion of a network section with loads storage systems and renewable generation sources into a smart microgrid. Appl Sci. 11(11):5012. doi: 10.3390/app11115012.
  • Jung J, Han H, Kim K, Kim HS. 2021. Machine learning-based small hydropower potential prediction under climate change. Energies. 14(12):3643. doi: 10.3390/en14123643.
  • Kabo-Bah AT, Diji CJ, Nokoe K, Mulugetta Y, Obeng-Ofori D, Akpoti K. 2016. Multiyear rainfall and temperature trends in the Volta river basin and their potential impact on hydropower generation in Ghana. Climate. 4(4):49. doi: 10.3390/cli4040049.
  • Karri RR, Wang X, Gerritsen H. 2014. Ensemble based prediction of water levels and residual currents in Singapore regional waters for operational forecasting. Environ Model Softw. 54:24–38. doi: 10.1016/j.envsoft.2013.12.006.
  • Kingma DP, Ba J. 2014. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  • Komer B, Bergstra J, Eliasmith C. 2014. Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn. ICML workshop on AutoML. (9) Austin (TX): Citeseer. doi: 10.25080/Majora-14bd3278-006.
  • Lopes MNG, da Rocha BRP, Vieira AC, de Sá JAS, Rolim PAM, da Silva AG. 2019. Artificial neural networks approaches for predicting the potential for hydropower generation: a case study for Amazon region. IFS. 36(6):5757–5772. doi: 10.3233/JIFS-181604.
  • Özbay FA, Özbay E. 2023. An NCA-based hybrid cnn model for classification of Alzheimer’s disease on grad-cam-enhanced brain MRI images. Turkish J Sci Technol. 18(1):139–155. doi: 10.55525/tjst.1212513.
  • Pan T, Wu S, Dai E, Liu Y. 2013. Estimating the daily global solar radiation spatial distribution from diurnal temperature ranges over the Tibetan Plateau in China. Appl Energy. 107:384–393. doi: 10.1016/j.apenergy.2013.02.053.
  • Pan H, Lv X. 2019. Reconstruction of spatially continuous water levels in the Columbia river estuary: the method of empirical orthogonal function revisited. Estuar Coast Shelf Sci. 222:81–90. doi: 10.1016/j.ecss.2019.04.011.
  • Perera A, Rathnayake U. 2020. Relationships between hydropower generation and rainfall-gauged and ungauged catchments from Sri Lanka. Math Prob Eng. 2020:1–8. doi: 10.1155/2020/9650251.
  • Piri J, Shamshirband S, Petković D, Tong CW, Ur Rehman MH. 2015. Prediction of the solar radiation on the earth using support vector regression technique. Infrared Phys Technol. 68:179–185. doi: 10.1016/j.infrared.2014.12.006.
  • Plucinski B, Sun Y, Wang SY, Gillies RR, Eklund J, Wang CC. 2019. Feasibility of multi-year forecast for the Colorado river water supply: time series modeling. Water. 11(12):2433. doi: 10.3390/w11122433.
  • Rahman MM, Shakeri M, Tiong SK, Khatun F, Amin N, Pasupuleti J, Hasan MK. 2021. Prospective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networks. Sustainability. 13(4):2393. doi: 10.3390/su13042393.
  • Rodríguez F, Galarza A, Vasquez JC, Guerrero JM. 2022. Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control. Energy. 239:122116. doi: 10.1016/j.energy.2021.122116.
  • Rosenzweig C, Solecki WD, Hammer SA, Mehrotra S. 2011. Climate change and cities: first assessment report of the urban climate change research network. Cambridge, UK: Cambridge University Press.
  • Salcedo‐Sanz S, Rojo‐Álvarez JL, Martínez‐Ramón M, Camps‐Valls G. 2014. Support vector machines in engineering: an overview. WIREs Data Mining Knowl Discov. 4(3):234–267. doi: 10.1002/widm.1125.
  • Salcedo-Sanz S, Cornejo-Bueno L, Prieto L, Paredes D, García-Herrera R. 2018. Feature selection in machine learning prediction systems for renewable energy applications. Renew Sustain Energy Rev. 90:728–741. doi: 10.1016/j.rser.2018.04.008.
  • Sessa V, Assoumou E, Bossy M, Simões SG. 2021. Analyzing the applicability of random forest-based models for the forecast of run-of-river hydropower generation. Clean Technol. 3(4):858–880. doi: 10.3390/cleantechnol3040050.
  • Sharifzadeh F, Akbarizadeh G, Seifi Kavian Y. 2019. Ship classification in SAR images using a new hybrid CNN–MLP classifier. J Indian Soc Remote Sens. 47(4):551–562. doi: 10.1007/s12524-018-0891-y.
  • Shaw AR, Smith Sawyer H, LeBoeuf EJ, McDonald MP, Hadjerioua B. 2017. Hydropower optimization using artificial neural network surrogate models of a high-fidelity hydrodynamics and water quality model. Water Resour Res. 53(11):9444–9461. doi: 10.1002/2017WR021039.
  • Şahin ME, Ulutaş H, Yüce E. 2021. A deep learning approach for detecting pneumonia in chest X-rays. Eur J Sci Technol. 28:562–567. doi: 10.31590/ejosat.1009434.
  • Sorgula Ş. 2023. Available online: https://sehirsorgula.com/kirsehir-yol-tarifi/. (accessed on 04.02
  • Ulutaş H, Aslantaş V. 2023. Design of efficient methods for the detection of tomato leaf disease 522 utilizing proposed ensemble CNN model. Electronics. 12(4):827. doi: 10.3390/electronics12040827.
  • Yildiz C, Açikgöz H. 2021. Forecasting diversion type hydropower plant generations using an artificial bee colony based extreme learning machine method. Energy Sources B: Econ Plan Policy. 16(2):216–234. doi: 10.1080/15567249.2021.1872119.
  • Zhang X, Liu P, Zhao Y, Deng C, Li Z, Xiong M. 2018. Error correction-based forecasting of reservoir water levels: improving accuracy over multiple lead times. Environ Model Softw. 104:27–39. doi: 10.1016/j.envsoft.2018.02.017.
  • Zolfaghari M, Golabi MR. 2021. Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models. Renew Energy. 170:1367–1381. doi: 10.1016/j.renene.2021.02.017.