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.

通过开发机器和深度学习的 “后向预测” 模型，重建多十年需求记录，研究温度的自然变化以及其对需求的影响，以更好地支持电力系统技术和政策的发展过程。

长期历史需求数据的重建

Reconstruction of Long-Term Historical Demand Data

Trends in terrestrial temperature variability are perhaps more relevant for
species viability than trends in mean temperature. In this paper, we develop
methodology for estimating such trends using multi-resolution climate data from
polar orbiting weather satellites. We derive two novel algorithms for
computation that are tailored for dense, gridded observations over both space
and time. We evaluate our methods with a simulation that mimics these data's
features and on a large, publicly available, global temperature dataset with
the eventual goal of tracking trends in cloud reflectance temperature
variability.

本文提出使用极轨气象卫星的多分辨率气候数据来估计地球表面温度变化趋势的方法。通过开发两种专为密集网格观测设计的计算算法，我们评估了这些方法的性能，并计划跟踪云反射温度变化趋势。