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Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models

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  • Zolfaghari, Mehdi
  • Golabi, Mohammad Reza

Abstract

Electricity is an important pillar for the economic growth and the development of societies. Surveying and predicting the electricity production (EP) is a valuable factor in the hands of electricity industry managers to make strategic decisions, especially if electricity is generated by renewable resources for environmental considerations. However, because the EP series is non-stationary and nonlinear, traditional methods are less robust to predict it. In this study, we offer a hybrid model which combines adaptive wavelet transform (AWT), long short-term memory (LSTM) and random forest (RF) algorithm (AWT-LSTM-RF) to predict the EP in hydroelectric power plant. We also apply the exogenous affecting variables on EP in the structure of hybrid model, which were selected by ant colony optimization (ACO) algorithm. To evaluate the predictive power of the AWT-LSTM-RF model, we compared its predictive results with the benchmark models including RF, ARIMA-GARCH, wavelet transform-feed forward neural network (WT-FFNN), wavelet transform-random forest (WT-RF), wavelet transform-LSTM (WT-LSTM), and WT-FFNN-RF. The empirical results indicate that the hybrid model of AWT-LSTM-RF outperforms the benchmark models. The results also suggest that applying the wavelet transform on input data of the RF algorithm (WT-RF) can improve the predictive power of the RF.

Suggested Citation

  • Zolfaghari, Mehdi & Golabi, Mohammad Reza, 2021. "Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models," Renewable Energy, Elsevier, vol. 170(C), pages 1367-1381.
    • Handle: RePEc:eee:renene:v:170:y:2021:i:c:p:1367-1381
    DOI: 10.1016/j.renene.2021.02.017
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    Cited by:

    1. Jamali Jahromi, Ali & Mohammadi, Mohammad & Afrasiabi, Shahabodin & Afrasiabi, Mousa & Aghaei, Jamshid, 2022. "Probability density function forecasting of residential electric vehicles charging profile," Applied Energy, Elsevier, vol. 323(C).
    2. Yuan-Kang Wu & Cheng-Liang Huang & Quoc-Thang Phan & Yuan-Yao Li, 2022. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints," Energies, MDPI, vol. 15(9), pages 1-22, May.
    3. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    4. Du, Xiaoyi & Wu, Dongdong & Yan, Yabo, 2023. "Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China," Energy, Elsevier, vol. 262(PA).
    5. Li, Zekai & Hu, Xi & Guo, Huan & Xiong, Xin, 2023. "A novel Weighted Average Weakening Buffer Operator based Fractional order accumulation Seasonal Grouping Grey Model for predicting the hydropower generation," Energy, Elsevier, vol. 277(C).
    6. Dieudonné, Nzoko Tayo & Armel, Talla Konchou Franck & Hermann, Djeudjo Temene & Vidal, Aloyem Kaze Claude & René, Tchinda, 2023. "Optimization of Short-Term Forecast of Electric Power Demand in the city of Yaoundé-Cameroon by a hybrid model based on the combination of neural networks and econometric methods from a designed energ," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    7. Xiong, Xin & Hu, Xi & Tian, Tian & Guo, Huan & Liao, Han, 2022. "A novel Optimized initial condition and Seasonal division based Grey Seasonal Variation Index model for hydropower generation," Applied Energy, Elsevier, vol. 328(C).
    8. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    9. Diogo M. F. Izidio & Paulo S. G. de Mattos Neto & Luciano Barbosa & João F. L. de Oliveira & Manoel Henrique da Nóbrega Marinho & Guilherme Ferretti Rissi, 2021. "Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters," Energies, MDPI, vol. 14(7), pages 1-19, March.
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    11. Chen, Chao & Liang, Rui & Ge, Yadong & Li, Jian & Yan, Beibei & Cheng, Zhanjun & Tao, Junyu & Wang, Zhenyu & Li, Meng & Chen, Guanyi, 2022. "Fast characterization of biomass pyrolysis oil via combination of ATR-FTIR and machine learning models," Renewable Energy, Elsevier, vol. 194(C), pages 220-231.
    12. Mohammad Ehtearm & Hossein Ghayoumi Zadeh & Akram Seifi & Ali Fayazi & Majid Dehghani, 2023. "Predicting Hydropower Production Using Deep Learning CNN-ANN Hybridized with Gaussian Process Regression and Salp Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3671-3697, July.
    13. Pawan Kumar Singh & Alok Kumar Pandey & S. C. Bose, 2023. "A new grey system approach to forecast closing price of Bitcoin, Bionic, Cardano, Dogecoin, Ethereum, XRP Cryptocurrencies," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2429-2446, June.

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