  Long-term planning of a robust power system requires the understanding of
changing demand patterns. Electricity demand is highly weather sensitive. Thus,
the supply side variation from introducing intermittent renewable sources,
juxtaposed with variable demand, will introduce additional challenges in the
grid planning process. By understanding the spatial and temporal variability of
temperature over the US, the response of demand to natural variability and
climate change-related effects on temperature can be separated, especially
because the effects due to the former factor are not known. Through this
project, we aim to better support the technology & policy development process
for power systems by developing machine and deep learning 'back-forecasting'
models to reconstruct multidecadal demand records and study the natural
variability of temperature and its influence on demand.
