One-day ahead forecasting of energy production from run-of-river hydroelectric power plants with a deep learning approach

Document Type : Article

Authors

1 Department of Mechanical Engineering, Ceyhan Engineering Faculty, Cukurova University, Adana 01950, Turkey

2 Department of Automotive Engineering, Faculty of Engineering, Cukurova University, Adana 01380, Turkey

3 Department of Energy System Engineering, Faculty of Engineering, Adana Alparslan Turkes Science and Technology University, Adana 01250, Turkey

Abstract

Accurate energy production forecasting is critical when planning energy for the economic development of a country. A deep learning approach based on Long Short-Term Memory (LSTM) to forecast one-day-ahead energy production from the run-of-river hydroelectric power plants in Turkey was introduced in the present study. In addition to the LSTM network, three different data-driven methods, namely, adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM), ANFIS with subtractive clustering (SC), and ANFIS with grid partition (GP) were applied. The correlation coefficient (R), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used as quality metrics for prediction. Predicted values of the LSTM, ANFIS-FCM, ANFIS-SC, and ANFIS-GP models were compared with observed values by evaluating their errors. MAPE values in the testing process are 5.98%, 6.14%, 6.16%, and 6.40% for the LSTM neural network, ANFIS-FCM, ANFIS-SC, and ANFIS-GP models, respectively. The comparison revealed that the LSTM neural network provided high accuracy results in one day-ahead short-term energy production prediction and gave higher performance than other ANFIS models used in the study.

Keywords

References

References:
1. Stenberg, V., Ryden, M., Mattisson, T., et al. "Exploring novel hydrogen production processes by integration of steam methane reforming with chemicallooping combustion (CLC-SMR) and oxygen carrier aided combustion (OCAC-SMR)", Int. J. Greenh. Gas Control, 74 (May), pp. 28-39 (2018).
2. Chang, X.L., Liu, X., and Zhou, W. "Hydropower in China at present and its further development", Energy, 35(11), pp. 4400-4406 (2010).
3. Yuksek, o. "Reevaluation of Turkey's hydropower potential and electric energy demand", Energy Policy, 36(9), pp. 3374-3382 (2008).
4. REN21. https://www.ren21.net/. accessed November 30, 2020.
5. Dmitrieva, K. Forecasting of a Hydropower Plant Energy Production, Ostfold University College, MSc Thesis (2015).
6. Li, G., Liu, C.X., Liao, S.L., et al. "Applying a correlation analysis method to long-term forecasting of power production at small hydropower plants", Water (Switzerland), 7(9), pp. 4806-4820 (2015).
7. Wang, S., Yu, L., Tang, L., et al. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China", Energy, 36(11), pp. 6542-6554 (2011).
8. Ardakani, F.J. and Ardehali, M.M. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types", Energy, 65, pp. 452-461 (2014).
9. Gunay, M.E. "Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey", Energy Policy, 90, pp. 92-101 (2016).
10. Stokelj, T., Paravan, D., and Golob, R. "Enhanced artificial neural network in flow forecasting algorithm for run-of-river hydropower plants", J. Water Resour.Plan. Manag., 128(6), pp. 415-423 (2002). 
11. Campolo, M., Soldati, A., and Andreussi, P. "Forecasting river  flow rate during low-flow periods using neural networks", Water Resour. Res., 35(11), pp. 3547-3552 (1999).
12. Abdulkadir, T., Salami, A., Anwar, A., et al. "Modelling of hydropower reservoir variables for energy generation: Neural network approach", Ethiop. J. Environ. Stud. Manag., 6(3), pp. 310-316 (2013).
13. Igboanugo, A.C. "Predicting water levels at Kainji dam using artificial neural networks", Niger. J. Technol., 32(1), pp. 129-136 (2013).
14. Cobaner, M., Haktanir, T., and Kisi, O. "Prediction of hydropower energy using ANN for the feasibility of hydropower plant installation to an existing irrigation dam", Water Resour. Manag., 22(6), pp. 757-774 (2008).
15. Hammid, A.T., Sulaiman, M.H.B., and Abdalla, A.N. "Prediction of small hydropower plant power production in Himreen Lake dam (HLD) using artificial neural network", Alexandria Eng. J., 57(1), pp. 211-221 (2018).
16. Uzlu, E., Akpinar, A., Ozturk, H.T., et al. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey", Energy, 69, pp. 638-647 (2014).
17. Monteiro, C., Ramirz-Rosado, I.J., and Fernandez- Jimenez, L.A. "Short-term forecasting model for electric power production of small-hydro power plants", Renew. Energy, 50, pp. 387-394 (2013).
18. Monteiro, C., Ramirez-Rosado, I.J., and Fernandez-Jimenez, L.A. "Short-term forecasting model for aggregated regional hydropower generation", Energy Convers. Manag., 88, pp. 231-238 (2014).
19. Ahmad, S.K. and Hossain, F. "Maximizing energy production from hydropower dams using short-term weather forecasts", Renew. Energy, 146, pp. 1560-1577 (2020).
20. Mite-Leon, M. and Barzola-Monteses, J. "Statistical model for the forecast of hydropower production in Ecuador", Int. J. Renew. Energy Res., 8(2), pp. 1130- 1137 (2018).
21. Ediger, V.S. and Akar, S. "ARIMA forecasting of primary energy demand by fuel in Turkey", Energy Policy, 35(3), pp. 1701-1708 (2007).
22. Yuksek, O., Komurcu, M.I., Yuksel, I., et al. "The role of hydropower in meeting Turkey's electric energy demand", Energy Policy, 34(17), pp. 3093-3103 (2006).
23. Cheng, C.T., Miao, S.M., Luo, B., et al. "Forecasting monthly energy production of small hydropower plants in ungauged basins using grey model and improved seasonal index", J. Hydroinformatics, 19(6), pp. 993-1008 (2017).
24. Wang, Z.X., Li, Q., and Pei, L.L. "Grey forecasting method of quarterly hydropower production in China based on a data grouping approach", Appl. Math. Model., 51, pp. 302-316 (2017).
25. Gragne, A.S., Sharma, A., Mehrotra, R., et al. "Improving real-time in flow forecasting into hydropower reservoirs through a complementary modelling framework", Hydrol. Earth Syst. Sci., 19(8), pp. 3695-3714 (2015).
26. Li, G., Li, B.J., Yu, X.G., et al. "Echo state network with Bayesian regularization for forecasting shortterm power production of small hydropower plants", Energies, 8(10), pp. 12228-12241 (2015).
27. Lima, C.H.R. and Lall, U. "Climate informed monthly stream flow forecasts for the Brazilian hydropower network using a periodic ridge regression model", J. Hydrol., 380(3-4), pp. 438-449 (2010).
28. Operacz, A., Szelag, B., and Grahl-Madsen, M. "Possibility of the modelling of electricity production from hydropower", E3S Web Conf., 86 (2019).
29. Dehghani, M., Riahi-Madvar, H., Hooshyaripor, F., et al. "Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system", Energies, 12(2), pp. 1-20 (2019).
30. Chen, J., Zeng, G.Q., Zhou, W., et al. "Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization", Energy Convers. Manag., 165(March), pp. 681- 695 (2018).
31. Yu, C., Li, Y., Bao, Y., et al. "A novel framework for wind speed prediction based on recurrent neural networks and support vector machine", Energy Convers. Manag., 178(October), pp. 137-145 (2018).
32. Zhang, Z., Qin, H., Liu, Y., et al. "Long short-term memory network based on neighborhood gates for processing complex causality in wind speed prediction", Energy Convers. Manag., 192(January), pp. 37-51 (2019).
33. Liang, S., Nguyen, L., and Jin, F. "A multi-variable stacked long-short term memory network for wind speed forecasting", Proc. - 2018 IEEE Int. Conf. Big Data, Big Data 2018, pp. 4561-4564 (2019).
34. Wang, J., and Li, Y. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy", Appl. Energy, 230(April 2018), pp. 429-443 (2018).
35. Liu, H., Mi, X., and Li, Y. "Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM", Energy Convers. Manag., 159(November 2017), pp. 54-64 (2018).
36. Balluff, S., Bendfeld, J., and Krauter, S. "Short term wind and energy prediction for offshore wind farms using neural networks", Int. Conf. Renew. Energy Res. Appl. ICRERA, 5, pp. 379-382 (2015).
37. Shi, X., Lei, X., Huang, Q., et al. "Hourly dayahead  wind power prediction using the hybrid model of variational model decomposition and long short-term memory", Energies, 11(11), pp. 1-20 (2018).
38. Muzaffar, S., and Afshari, A. "Short-term load forecasts using LSTM networks", Energy Procedia, 158, pp. 2922-2927 (2019).
39. Arslan, N. and Sekertekin, A. "Application of long short-term memory neural network model for the reconstruction of MODIS land surface temperature images", J. Atmos. Solar-Terrestrial Phys., 194(June), p. 105100 (2019).
40. Han, S., Qiao, Y., Yan, J., et al. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network", Appl. Energy, 239(January), pp. 181-191 (2019).
41. Zhang, J., Yan, J., Infield, D., et al. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model", Appl. Energy, 241(October 2018), pp. 229-244 (2019).
42. Zhang, Z., Ye, L., Qin, H., et al. "Wind speed prediction method using shared weight long short-term memory network and Gaussian process regression", Appl. Energy, 247(March), pp. 270-284 (2019).
43. Abyaneh, H.Z., Nia, A.M., Varkeshi, M.B., et al. "Performance evaluation of ANN and ANFIS models for estimating garlic crop evapotranspiration", J. Irrig. Drain. Eng., 137(5), pp. 280-286 (2011).
44. Jang, J.-S.R. "ANFIS: adaptive-network-based fuzzy inference system", IEEE Trans. Syst. Man. Cybern., 23(3), pp. 665-685 (1993).
45. Karakus, O., Kuruoglu, E.E., and Altinkaya, M.A. "One-day ahead wind speed/power prediction based on polynomial autoregressive model", IET Renew. Power Gener., 11(11), pp. 1430-1439 (2017).
46. Tabari, H., Kisi, O., Ezani, A., et al. "SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment", J. Hydrol., 444(445), pp. 78-89 (2012).
47. Mathworks. https://ch.mathworks.com/solutions/ deep-learning.html. Accessed November 30, 2020.
48. Van Houdt, G., Mosquera, C., and Napoles, G. "A review on the long short-term memory model", Artif. Intell. Rev., 53(8), pp. 5929-5955 (2020).
49. Gers, F.A., Schraudolph, N.N., and Schmidhuber, J. "Learning precise timing with LSTM recurrent networks", J. Mach. Learn. Res., 3(1), pp. 115-143 (2003).
50. Hochreiter, S. and Schmidhuber, J. "Long shortterm memory", Neural Comput., 9(8), pp. 1735-1780 (1997).
51. Ekanayake, P., Wickramasinghe, L., Jayasinghe, J.M.J.W., et al. "Regression-based prediction of power generation at samanalawewa hydropower plant in Sri Lanka using machine learning", Math. Probl. Eng., 2021 (2021).
52. Jung, J., Han, H., Kim, K., et al. "Machine learningbased small hydropower potential prediction under climate change", Energies, 14(12) (2021).
53. Lotfi, E., Babrzadeh, S., and Khosravi, A. "Sensitivity analysis of economic variables using neuro-fuzzy approach", Sci. Iran., 27(3D), pp. 1352-1359 (2020).
54. Turkish Electricity Transmission Corporation, https://www.teias.gov.tr/. Accessed December 4, 2020.
55. Aksoy, B. "Estimation of energy produced in hydroelectric power plant industrial automation using deep learning and hybrid machine learning techniques", Electr. Power Components Syst., 49(3), pp. 213-232 (2021).
56. Filho, A.R.G., Silva, D.F.C., de Carvalho, R.V., et al. "Forecasting of water  flow in a hydroelectric power plant using LSTM recurrent neural network", 2nd Int. Conf. Electr. Commun. Comput. Eng. (2020).
57. Ogliari, E., Nespoli, A., Mussetta, M., et al. "A hybrid method for the run-of-the-river hydroelectric power plant energy forecast: HYPE hydrological model and neural network", Forecasting, 2(4), pp. 410-428 (2020).
58. Huangpeng, Q., Huang, W., and Gholinia, F. "Forecast of the hydropower generation under influence of climate change based on RCPs and developed crow search optimization algorithm", Energy Reports, 7, pp. 385-397 (2021).
Scientia Iranica
Volume 29, Issue 4
Transactions on Mechanical Engineering (B)
July and August 2022
Pages 1838-1852

Files

Share

How to cite

Statistics