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Obtaining good initial conditions to solve the Newton-Raphson (NR) based acpower flow (ACPF) problem can be a very difficult task. In this paper, wepropose a framework to obtain the initial bus voltage magnitude and phasevalues that decrease the solution iterations and time for the NR based ACPFmodel, using the dc power flow (DCPF) results and one dimensional convolutionalneural networks (1D CNNs). We generate the dataset used to train the 1D CNNs bysampling from a distribution of load demands, and by computing the DCPF andACPF results for each sample. Experiments on the IEEE 118-bus and\textsc{Pegase} 2869-bus study systems show that we can achieve 33.56\% and30.06\% reduction in solution time, and 66.47% and 49.52% reduction in solutioniterations per case, respectively. We include the 1D CNN architectures and thehyperparameters used, which can be expanded on by the future studies on thistopic.

Hot-Starting the Ac Power Flow with Convolutional Neural Networks