  Electric vehicles can offer a low carbon emission solution to reverse rising
emission trends. However, this requires that the energy used to meet the demand
is green. To meet this requirement, accurate forecasting of the charging demand
is vital. Short and long-term charging demand forecasting will allow for better
optimisation of the power grid and future infrastructure expansions. In this
paper, we propose to use publicly available data to forecast the electric
vehicle charging demand. To model the complex spatial-temporal correlations
between charging stations, we argue that Temporal Graph Convolution Models are
the most suitable to capture the correlations. The proposed Temporal Graph
Convolutional Networks provide the most accurate forecasts for short and
long-term forecasting compared with other forecasting methods.
