  This paper develops an ensemble learning-based linearization approach for
power flow, which differs from the network-parameter based direct current (DC)
power flow or other extended versions of linearization. As a novel data-driven
linearization through data mining, it firstly applies the polynomial regression
(PR) as a basic learner to capture the linear relationships between the bus
voltage as the independent variable and the active or reactive power as the
dependent variable in rectangular coordinates. Then, gradient boosting (GB) and
bagging as ensemble learning methods are introduced to combine all basic
learners to boost the model performance. The fitted linear power flow model is
also relaxed to compute the optimal power flow (OPF). The simulating results of
standard IEEE cases indicate that (1) ensemble learning methods outperform PR
and GB works better than bagging; (2) as for solving OPF, the data-driven model
excels the DC model and the SDP relaxation in the computational accuracy, and
works faster than ACOPF and SDPOPF.
