Reconstruction of Long-Term Historical Demand Data
This work addresses the need for robust power system planning by providing insights into demand patterns for policymakers and grid operators, though it appears incremental as it applies existing methods to new data.
The researchers tackled the problem of reconstructing long-term historical electricity demand data to understand natural temperature variability and climate change effects, aiming to support power system planning and policy development.
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.