The article discusses the flexibility of Distributed Power Flow Learning (DPFL) methods in contrast to traditional Pointwise Power Flow Learning (PPFL) methods. It highlights how DPFL can utilize arbitrary known variables such as power levels and voltage of buses, while also predicting unknown variables. This increases the applicability and generalizability of models in complex power systems, demonstrating a significant advantage in adapting to various scenarios, including those involving multicollinearity and constant predictors.
"DPFL methods generally offer a more flexible framework compared to PPFL methods, allowing for the use of arbitrary known and unknown variables as predictors in modeling."
"The flexibility of DPFL approaches showcases their potential in accommodating a wider range of variables, enhancing the adaptability of predictive modeling in power systems."
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