Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project
نویسندگان
چکیده
Abstract Energy use forecasting is crucial in balancing the electricity supply and demand to reduce uncertainty inherent inter-basin water transfer project. prediction supports reliable water-energy encourages cost-effective operation by improving generation scheduling. The objectives are develop subsequent monthly energy predictive models for Mokelumne River Aqueduct California, US. Partial (a) compare model performance of a baseline (multiple linear regression (MLR)) three machine learning-based (random forest (RF), deep neural network (DNN), support vector (SVR)), (b) whole system subsystems (conveyance, treatment, distribution), (c) conduct sensitivity analysis. We simulate total 64 cases (4 algorithms (MLR, RF, DNN, SVR) x 4 systems (whole, conveyance, distribution) scenarios (different combinations independent variables). concluded that learning showed better than as they reflected non-linear characteristics systems. Among algorithms, DNN yielded higher RF SVR models. Subsystems performed more closely unique subsystems. best case was having (t), (t-1), precipitation temperature population (y) variables. These can help utility managers understand enhance efficiency their
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ژورنال
عنوان ژورنال: Water Resources Management
سال: 2022
ISSN: ['0920-4741', '1573-1650']
DOI: https://doi.org/10.1007/s11269-022-03326-7