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The load demand for each hour serves as the input dataset for GRZPNet, which can be properly trained utilizing the historic demand datasets. At the time properly trained, the community generates optimum demand outputs, reflecting the variation in demand for each hour. The QI-NLS-G2O technique is adopted for training the training design, as describe