Reconstruction of Long-Term Historical Demand Data

TL;DR

This paper develops machine learning models to reconstruct long-term electricity demand data, accounting for temperature variability and climate change effects, to improve power system planning.

Contribution

It introduces novel back-forecasting models that separate natural temperature variability from climate change impacts on demand.

Findings

Successfully reconstructed multidecadal demand records

Analyzed the influence of temperature variability on demand

Provided insights for power system planning under climate change

Abstract

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.