  The increasing adoption of measurement units in electrical power distribution
grids has enabled the deployment of data-driven and measurement-based control
schemes. Such schemes rely on measurement-based estimated models, where the
models are first estimated using raw measurements and then used in the control
problem. This work focuses on measurement-based estimation of the voltage
sensitivity coefficients which can be used for voltage control. In the existing
literature, these coefficients are estimated using regression-based methods,
which do not perform well in the case of high measurement noise. This work
proposes tackling this problem by using neural network (NN)-based estimation of
the voltage sensitivity coefficients which is robust against measurement noise.
In particular, we propose using Feedforward and Long-Short Term Memory (LSTM)
neural networks. The trained NNs take measurements of nodal voltage magnitudes
and active and reactive powers and output the vector of voltage magnitude
sensitivity coefficients. The performance of the proposed scheme is compared
against the regression-based method for a CIGRE benchmark network.
