  Obtaining good initial conditions to solve the Newton-Raphson (NR) based ac
power flow (ACPF) problem can be a very difficult task. In this paper, we
propose a framework to obtain the initial bus voltage magnitude and phase
values that decrease the solution iterations and time for the NR based ACPF
model, using the dc power flow (DCPF) results and one dimensional convolutional
neural networks (1D CNNs). We generate the dataset used to train the 1D CNNs by
sampling from a distribution of load demands, and by computing the DCPF and
ACPF results for each sample. Experiments on the IEEE 118-bus and
\textsc{Pegase} 2869-bus study systems show that we can achieve 33.56\% and
30.06\% reduction in solution time, and 66.47% and 49.52% reduction in solution
iterations per case, respectively. We include the 1D CNN architectures and the
hyperparameters used, which can be expanded on by the future studies on this
topic.
