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Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project

Author

Listed:
  • Sooyeon Yi

    (University of California)

  • G. Mathias Kondolf

    (University of California)

  • Samuel Sandoval-Solis

    (University of California)

  • Larry Dale

    (University of California)

Abstract

Energy use forecasting is crucial in balancing the electricity supply and demand to reduce the uncertainty inherent in the inter-basin water transfer project. Energy use prediction supports the reliable water-energy supply and encourages cost-effective operation by improving generation scheduling. The objectives are to develop subsequent monthly energy use predictive models for the Mokelumne River Aqueduct in California, US. Partial objectives are to (a) compare the model performance of a baseline model (multiple linear regression (MLR)) to three machine learning-based models (random forest (RF), deep neural network (DNN), support vector regression (SVR)), (b) compare the model performance of the whole system to three subsystems (conveyance, treatment, distribution), and (c) conduct sensitivity analysis. We simulate a total of 64 cases (4 algorithms (MLR, RF, DNN, SVR) x 4 systems (whole, conveyance, treatment, distribution) x 4 scenarios (different combinations of independent variables). We concluded that the three machine learning algorithms showed better model performance than the baseline model as they reflected non-linear energy use characteristics for water transfer systems. Among the three machine learning algorithms, DNN models yielded higher model performance than RF and SVR models. Subsystems performed better than the whole system as the models more closely reflected the unique energy use characteristics of the subsystems. The best case was having water supply (t), water supply (t-1), precipitation (t), temperature (t), and population (y) as independent variables. These models can help water and energy utility managers to understand energy performance better and enhance the energy efficiency of their water transfer systems.

Suggested Citation

  • Sooyeon Yi & G. Mathias Kondolf & Samuel Sandoval-Solis & Larry Dale, 2022. "Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5675-5694, November.
  • Handle: RePEc:spr:waterr:v:36:y:2022:i:14:d:10.1007_s11269-022-03326-7
    DOI: 10.1007/s11269-022-03326-7
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    Cited by:

    1. Youngje Choi & Jungwon Ji & Eunkyung Lee & Sunmi Lee & Sooyeon Yi & Jaeeung Yi, 2023. "Developing Optimal Reservoir Rule Curve for Hydropower Reservoir with an add-on Water Supply Function Using Improved Grey Wolf Optimizer," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(5), pages 2063-2082, March.
    2. Sarah Di Grande & Mariaelena Berlotti & Salvatore Cavalieri & Roberto Gueli, 2024. "A Machine Learning Approach to Forecasting Hydropower Generation," Energies, MDPI, vol. 17(20), pages 1-22, October.

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