Short-Term Electricity Load Forecasting Using the Temporal Fusion
  Transformer: Effect of Grid Hierarchies and Data Sources
Elena Giacomazzi Felix Haag Konstantin Hopf
Recent developments related to the energy transition pose particular
challenges for distribution grids. Hence, precise load forecasts become more
and more important for effective grid management. Novel modeling approaches
such as the Transformer architecture, in particular the Temporal Fusion
Transformer (TFT), have emerged as promising methods for time series
forecasting. To date, just a handful of studies apply TFTs to electricity load
forecasting problems, mostly considering only single datasets and a few
covariates. Therefore, we examine the potential of the TFT architecture for
hourly short-term load forecasting across different time horizons (day-ahead
and week-ahead) and network levels (grid and substation level). We find that
the TFT architecture does not offer higher predictive performance than a
state-of-the-art LSTM model for day-ahead forecasting on the entire grid.
However, the results display significant improvements for the TFT when applied
at the substation level with a subsequent aggregation to the upper grid-level,
resulting in a prediction error of 2.43% (MAPE) for the best-performing
scenario. In addition, the TFT appears to offer remarkable improvements over
the LSTM approach for week-ahead forecasting (yielding a predictive error of
2.52% (MAPE) at the lowest). We outline avenues for future research using the
TFT approach for load forecasting, including the exploration of various grid
levels (e.g., grid, substation, and household level).