The forecast module (ic-fc) is conceived to provide a clear characterization of the most common forecasting data. During the ontology requirement specification process with the InterConnect partners, we noticed that a distinction was needed between point forecasts versus stochastic forecasts, as well as the various ways to express stochastic forecasts. Therefore, the ic-fc module takes into account this distinction. It reuses the ic-data module, which defines time-series and data-points that are important elements of forecasting. It also reuses the ic-topology module, which defines the forecast location (geographical but also topological, for example, the grid segment). The following figure shows the concepts that we designed for defining forecasts.
The forecast module (ic-fc) is conceived to provide a clear characterization of the most common forecasting data. During the ontology requirement specification process with the InterConnect partners, we noticed that a distinction was needed between point forecasts versus stochastic forecasts, as well as the various ways to express stochastic forecasts. Therefore, the ic-fc module takes into account this distinction. It reuses the ic-data module, which defines time-series and data-points that are important elements of forecasting. It also reuses the ic-topology module, which defines the forecast location (geographical but also topological, for example, the grid segment). The following figure shows the concepts that we designed for defining forecasts.
-`ic-fc:StochasticForecast` is the base type for all forecasts that have stochastic or probabilistic data points. This means we place restrictions on the type of data points a stochastic forecast consists of. We have three predefined stochastic forecasts.
-`ic-fc:StochasticForecast` is the base type for all forecasts that have stochastic or probabilistic data points. This means we place restrictions on the type of data points a stochastic forecast consists of. We have three predefined stochastic forecasts.
