Extending Load Forecasting from Zonal Aggregates to Individual Nodes for Transmission System Operators

The reliability of local power grid infrastructure is challenged by sustainable energy developments increasing electric load uncertainty. Transmission System Operators (TSOs) need load forecasts of higher spatial resolution, extending current forecasting operations from zonal aggregates to individual nodes. However, nodal loads are less accurate to forecast and require a large number of individual forecasts, which are hard to manage for the human experts assessing risks in the control room's daily operations (operator). In collaboration with a TSO, we design a multi-level system that meets the needs of operators for hourly day-ahead load forecasting. Utilizing a uniquely extensive dataset of zonal and nodal net loads, we experimentally evaluate our system components. First, we develop an interpretable and scalable forecasting model that allows for TSOs to gradually extend zonal operations to include nodal forecasts. Second, we evaluate solutions to address the heterogeneity and volatility of nodal load, subject to a trade-off. Third, our system is manageable with a fully parallelized single-model forecasting workflow. Our results show accuracy and interpretability improvements for zonal forecasts, and substantial improvements for nodal forecasts. Keywords: Short-Term Load Forecast, Transmission System Operator, Global Forecasting Model, Hierarchical Forecasting, Distributed Energy Resources, Electrical Power Grid1. Introduction Electric transmission system operators (TSOs) face increasing volatility in electric load due to distributed and renewable energy generation, climate events, and electrification [1]. This volatility complicates load forecasting, which is essential to TSO operations. TSOs must ensure that electricity generation matches load at all times, and the distribution of power across their territory does not overwhelm any infrastructure component. To accomplish this, they use day-ahead load forecasts to inform where to dispatch generators each hour of the coming day. Growing electrification and distributed generation increase volatility of'net load' - local consumption minus generation - in some places and not others, as adoption of these technologies proceeds unevenly. This could put a TSO's medium-voltage grid components, for example sub-transmission lines and primary distribution substations, at risk of damage if load forecasts miss unexpected local changes [2, 3, 4].