February 18, 2023
tell: A Python package to model future total electricity loads
AbstractProjecting changes in electricity demand (load) in response to anthropogenic and natural stressors is necessary for promoting energy system resilience. Given the pressures of aging infrastructure and the increasing integration of renewables, realistic load projections are critical for maintaining a stable grid and as a basis for long-term planning. Recently there have been advances in both short-term (minutes to hours ahead) and long-term (months to years ahead) load forecasting approaches. The general structure of these types of models are, understandably, quite different. Short- and medium-term load models most commonly relate meteorology and day-of-week parameters to loads. Longer-term models also use meteorology/climate as explanatory variables, but typically require bringing in “macro” variables like the decadal evolution of population or economic indicators. The Total ELectricity Load (TELL) model integrates aspects of both short- and long-term projections of electricity demand in a coherent and scalable way. TELL takes as input gridded hourly time-series of meteorology and uses the temporal variations in weather to project hourly time-series of total electricity demand for every county in the conterminous United States (CONUS) using a series of multilayer perceptron (MLP) models. Hourly projections from TELL are scaled to match the annual state-level total electricity demands projected by the U.S. version of the Global Change Analysis Model (GCAM-USA). GCAM-USA is designed to capture the long-term co-evolution of human-Earth systems. This unique approach allows TELL to reflect changes in the shape of the load profile due to variations in weather and climate as well as the long-term evolution of energy demand due to changes in population, technology, and economics.
Published: February 18, 2023