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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

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  • Hou, Rui
  • Li, Shanshan
  • Wu, Minrong
  • Ren, Guowen
  • Gao, Wei
  • Khayatnezhad, Majid
  • gholinia, Fatemeh

Abstract

This study evaluates the effect of climate change on electricity generation, electricity demand, and GHG emissions. For this purpose, using climate scenarios RCPs changes of climatic parameters are predicted. Due to the high importance of energy demand in the management of energy generation resources innovation research is related to forecasting electricity demand. The novelty is the use of an Artificial Neural Network optimized to predict the energy demand. To optimize the ANN method, the Improved Pathfinder algorithm has been used. The use of the optimization method in the ANN method provides a model with more precision and fewer errors for the prediction of energy demand. The results showed that due to the weather changes, hydropower generation for the near future under RCP2.6, RCP4.5, and RCP8.5 increases by about 2.765 MW, 1.892 MW, and 1.219 MW and for the far future increases by about 3.430 MW, 2.475 MW, and 1.827 MW. The electricity demand forecasting by The ANN-IPF model for the near and far future will increase compared to the base period of 391.9 MW and 716.65 MW, respectively. Therefore, the gap between the demand the power supply will increase. Using other resources, the difference between demand and power supply will decrease.

Suggested Citation

  • Hou, Rui & Li, Shanshan & Wu, Minrong & Ren, Guowen & Gao, Wei & Khayatnezhad, Majid & gholinia, Fatemeh, 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, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:energy:v:237:y:2021:i:c:s0360544221018697
    DOI: 10.1016/j.energy.2021.121621
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    Cited by:

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    2. Wei, Zhichen & Calautit, John, 2023. "Predictive control of low-temperature heating system with passive thermal mass energy storage and photovoltaic system: Impact of occupancy patterns and climate change," Energy, Elsevier, vol. 269(C).
    3. AL-Alimi, Dalal & AlRassas, Ayman Mutahar & Al-qaness, Mohammed A.A. & Cai, Zhihua & Aseeri, Ahmad O. & Abd Elaziz, Mohamed & Ewees, Ahmed A., 2023. "TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets," Applied Energy, Elsevier, vol. 343(C).
    4. Dan Jiang & Rui Hua & Jian Shao, 2022. "Ecological Evaluation of Sponge City Landscape Design Based on Aquatic Plants Application," Land, MDPI, vol. 11(11), pages 1-10, November.
    5. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
    6. Hamed, Mohammad M. & Ali, Hesham & Abdelal, Qasem, 2022. "Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances," Energy, Elsevier, vol. 255(C).
    7. 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.

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