Resolution (for Hierarchical Load Forecasting) and Resolution (for Probabilistic Load Forecasting)

During the past several decades, utilities have been developing long term load forecasts mostly using monthly data aggregated up to revenue class level or higher. Deployment of smart grid technologies allows utilities to collect data with hourly or sub-hourly interval at household level. Using these "high resolution" data, we can develop load forecasts at various levels in the system, which is called hierarchical load forecasting. There are two aspects of resolution in hierarchical load forecasting:

• Spatial resolution

Spatial resolution means how many points are being measured in a piece of land. In my master thesis on spatial load forecasting, I divided the service territory of a medium sized utility into 3460 small areas, about 50 acres each. The data was from transformer load management system. In today's world, a "small area" can be 0.2 acre (the size of a typical single family home) or smaller.  While short term load forecasts have been mostly developed based on hourly or half-hourly data, having load information at low levels can help enhance the forecasting accuracy (See One Size No Longer Fits All: Electric Load Forecasting with a Geographic Hierarchy).

• Temporal resolution

Temporal resolution means the sampling frequency of the meters. In my 2014 TSG paper, a major contribution was to demonstrate the additional forecasting accuracy gained by using high resolution data.

In probabilistic load forecasting, resolution refers to how the size of prediction interval varies at different time periods. A high-resolution probabilistic forecast can properly quantify the uncertainties at different time periods by providing the prediction interval with variable size. For instance, in the figure below, the prediction interval of summer months is much narrower than that of winter months, which tells that load is much more uncertain in winter than in summer.

Back to Load Forecasting Terminology.