Abstract
The hydropower industry is one of the most important sources of clean energy. Predicting hydropower production is essential for the hydropower industry. This study introduces a hybrid deep learning model to predict hydropower production. Statistical methods are unsuitable for modeling hydropower production because their accuracy depends on seasonal and periodic fluctuations. For accurate predictions, deep learning models can capture daily, weekly, and monthly patterns. Since ANNs may not capture latent and nonlinear patterns, we use deep learning models to predict hydropower production. We used Convolutional Neural Network-Multilayer Perceptron-Gaussian Process Regression (CNNE-MUPE-GPRE) to extract key features and predict outcomes. The main advantages of the hybrid model are the quantification of production uncertainty, the accurate prediction of hydropower production, and the extraction of features from input data. We use a binary SSOA to select optimal input scenarios. The new model is benchmarked against the long short term memory neural network (LSTM), Bi directional LSTM (BI-LSTM), MUPE, GPRE, MUPE-GPRE, CNNE-GPRE, and CNNE-MUPE models. The models are used to predict 1-, 2-, and 3-day ahead power. The root mean square error values of CNNE-MUPE-GPRE, CNNE-MUPE, CNNE-GPRE, MUPE-GPRE, BI-LSTM, LSTM, CNNE, MUPE, GPRE were 578, 615, 832, 861, 914, 934, 1436, 1712, and 1954 KW at the 1-day prediction horizon. The RMSE of the CNNE-MUPE-GPRE was 595, 600, and 612 at the 1-day, 2-days, and 3-days prediction horizons. Extending the prediction horizon degrades accuracy. The uncertainty of the CNNE-MUPE-GPRE model was lower than that of the other models. The CNNE-MUPE-GPRE model is recommended for more accurate hydropower production predictions.
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References
Azzouni A, Pujolle G (2017) A long short-term memory recurrent neural network framework for network traffic matrix prediction. arXiv preprint arXiv:1705.05690
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. https://doi.org/10.1007/s00521-021-06746-5
Dang BT, Truong TK (2022) Binary salp swarm algorithm for discounted 0–1 knapsack problem. PLoS ONE 17(4):e0266537
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. https://doi.org/10.3390/en12020289
Ehteram M, Mousavi SF, Karami H, Farzin S, Emami M, Othman FB, El-Shafie A (2017) Fast convergence optimization model for single and multi-purposes reservoirs using hybrid algorithm. Adv Eng Inform 32:287–298
Ehteram M, Karami H, Farzin S (2018a) Reservoir optimization for energy production using a new evolutionary algorithm based on multi-criteria decision-making models. Water Resour Manage 32(7):2539–2560
Ehteram M, Karami H, Farzin S (2018b) Reducing irrigation deficiencies based optimizing model for multi-reservoir systems utilizing spider monkey algorithm. Water Resour Manage 32(7):2315–2334
Ehteram M, Khozani ZS, Soltani-Mohammadi S, Abbaszadeh M (2023) Structure of Different Kinds of ANN Models. In Estimating Ore Grade Using Evolutionary Machine Learning Models (pp. 13–26). Singapore: Springer Nature Singapore
Fallah A, Rakhshandehroo GR, Berg POS, Orth R (2020) Evaluation of precipitation datasets against local observations in southwestern Iran. Int J Climatol 40(9):4102–4116
Faris H, Mafarja MM, Heidari AA, Aljarah I, Al-Zoubi AM, Mirjalili S, Fujita H (2018) An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2018.05.009
Gao M, Li J, Hong F, Long D (2019) Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM. Energy. https://doi.org/10.1016/j.energy.2019.07.168
Guo L, Chen J, Wu F, Wang M (2018) An electric power generation forecasting method using support vector machine. Syst Sci Control Eng. https://doi.org/10.1080/21642583.2018.1544947
Hanoon MS, Ahmed AN, Razzaq A, Oudah AY, Alkhayyat A, Huang YF, El-Shafie A (2022) Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China. Ain Shams Eng J 101919
He YL, Chen L, Gao Y, Ma JH, Xu Y, Zhu QX (2022) Novel double-layer bidirectional LSTM network with improved attention mechanism for predicting energy consumption. ISA Trans. https://doi.org/10.1016/j.isatra.2021.08.030
Heidari AA, Yin Y, Mafarja M, Jalali SMJ, Dong JS, Mirjalili S (2020). Efficient moth-flame-based neuroevolution models. https://doi.org/10.1007/978-981-32-9990-0_4
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Hou R, Li S, Wu M, Ren G, Gao W, Khayatnezhad M, gholinia, F. (2021) Assessing of impact climate parameters on the gap between hydropower supply and electricity demand by RCPs scenarios and optimized ANN by the improved Pathfinder (IPF) algorithm. Energy. https://doi.org/10.1016/j.energy.2021.121621
Huang Y, Liu S, Yang L (2018) Wind speed forecasting method using EEMD and the combination forecasting method based on GPR and LSTM. Sustainability (Switzerland). https://doi.org/10.3390/su10103693
Imrana Y, Xiang Y, Ali L, Abdul-Rauf Z (2021) A bidirectional LSTM deep learning approach for intrusion detection. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2021.115524
Jamei M, Ali M, Malik A, Prasad R, Abdulla S, Yaseen ZM (2022) Forecasting daily flood water level using hybrid advanced machine learning based time-varying filtered empirical mode decomposition approach. Water Resour Manage 36(12):4637–4676
Jaseena KU, Kovoor BC (2021) Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks. Energy Convers Manage. https://doi.org/10.1016/j.enconman.2021.113944
Li BJ, Sun GL, Liu Y, Wang WC, Huang XD (2022) Monthly runoff forecasting using variational mode decomposition coupled with gray wolf optimizer-based long short-term memory neural networks. Water Resour Manage 36(6):2095–2115
Liu Y, Pu H, Sun DW (2021) Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices. Trends Food Sci Technol. https://doi.org/10.1016/j.tifs.2021.04.042
Panahi F, Ehteram M, Ahmed AN, Huang YF, Mosavi A, El-Shafie A (2021) Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging. Ecol Ind 133:108285
Panda N, Majhi SK (2020) Improved salp swarm algorithm with space transformation search for training neural network. Arab J Sci Eng. https://doi.org/10.1007/s13369-019-04132-x
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 (Switzerland). https://doi.org/10.3390/su13042393
Samantaray S, Sawan Das S, Sahoo A, Prakash Satapathy D (2022) Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm. Ain Shams Eng J. https://doi.org/10.1016/j.asej.2022.101732
Seifi A, Ehteram M, Soroush F, Haghighi AT (2022) Multi-model ensemble prediction of pan evaporation based on the Copula Bayesian Model Averaging approach. Eng Appl Artif Intell 114:105124
Shadrin D, Nikitin A, Tregubova P, Terekhova V, Jana R, Matveev S, Pukalchik M (2021) An automated approach to groundwater quality monitoring-geospatial mapping based on combined application of gaussian process regression and bayesian information criterion. Water (Switzerland). https://doi.org/10.3390/w13040400
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
Sinitsin V, Ibryaeva O, Sakovskaya V, Eremeeva V (2022) Intelligent bearing fault diagnosis method combining mixed input and hybrid CNN-MLP model. Mech Syst Signal Process 180:109454
Sun Q, Tang Z, Gao J, Zhang G (2022) Short-term ship motion attitude prediction based on LSTM and GPR. Appl Ocean Res. https://doi.org/10.1016/j.apor.2021.102927
Tang S, Zhu Y, Yuan S (2021) An improved convolutional neural network with an adaptable learning rate towards multi-signal fault diagnosis of hydraulic piston pump. Adv Eng Inform. https://doi.org/10.1016/j.aei.2021.101406
Wang H, Zhang YM, Mao JX, Wan HP (2020a) A probabilistic approach for short-term prediction of wind gust speed using ensemble learning. J Wind Eng Ind Aerodyn. https://doi.org/10.1016/j.jweia.2020.104198
Wang W, Zhou C, He H, Wu W, Zhuang W, Shen XS (2020b) Cellular traffic load prediction with LSTM and Gaussian process regression. IEEE International Conference on Communications. https://doi.org/10.1109/ICC40277.2020.9148738
Wang Y, Feng B, Hua QS, Sun L (2021) Short-term solar power forecasting: a combined long short-term memory and gaussian process regression method. Sustainability (Switzerland). https://doi.org/10.3390/su13073665
Zha W, Liu Y, Wan Y, Luo R, Li D, Yang S, Xu Y (2022) Forecasting monthly gas field production based on the CNN-LSTM model. Energy 124889
Zhang H, Cai Z, Ye X, Wang M, Kuang F, Chen H, Li C, Li Y (2022) A multi-strategy enhanced salp swarm algorithm for global optimization. Eng Comput. https://doi.org/10.1007/s00366-020-01099-4
Zhao M, Fu X, Zhang Y, Meng L, Tang B (2022) Highly imbalanced fault diagnosis of mechanical systems based on wavelet packet distortion and convolutional neural networks. Adv Eng Inform 51:101535
Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B (2016). Attention-based bidirectional long short-term memory networks for relation classification. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers), pp. 207–212
Zou Z, Ergan S (2023) Towards emotionally intelligent buildings: a Convolutional neural network based approach to classify human emotional experience in virtual built environments. Adv Eng Inform 55:101868
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
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Formal analysis: Hossein Ghayoumi Zadeh, Moahammad Ehteram, writing, review, and editing: Ali Fayazi, Mohammad Ehteram, Akram Seifi, Majid Dehghani.
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Ehtearm, M., Ghayoumi Zadeh, H., Seifi, A. et al. Predicting Hydropower Production Using Deep Learning CNN-ANN Hybridized with Gaussian Process Regression and Salp Algorithm. Water Resour Manage 37, 3671–3697 (2023). https://doi.org/10.1007/s11269-023-03521-0
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DOI: https://doi.org/10.1007/s11269-023-03521-0