Hierarchical Demand Forecasting Benchmark for the Distribution Grid
This provides a domain-specific benchmark for forecasting algorithms in electrical grid management, but it is incremental as it focuses on comparing existing techniques.
The paper tackled the problem of predicting electrical load at various levels in a low voltage distribution grid using probabilistic forecasting techniques, resulting in a comparative evaluation with standard KPIs and public datasets to benchmark methods for demand-side management.
We present a comparative study of different probabilistic forecasting techniques on the task of predicting the electrical load of secondary substations and cabinets located in a low voltage distribution grid, as well as their aggregated power profile. The methods are evaluated using standard KPIs for deterministic and probabilistic forecasts. We also compare the ability of different hierarchical techniques in improving the bottom level forecasters' performances. Both the raw and cleaned datasets, including meteorological data, are made publicly available to provide a standard benchmark for evaluating forecasting algorithms for demand-side management applications.