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Short-term forecasts of energy consumption are invaluable for the operationof energy systems, including low voltage electricity networks. However, networkloads are challenging to predict when highly desegregated to small numbers ofcustomers, which may be dominated by individual behaviours rather than thesmooth profiles associated with aggregate consumption. Furthermore,distribution networks are challenged almost entirely by peak loads, and taskssuch as scheduling storage and/or demand flexibility maybe be driven bypredicted peak demand, a feature that is often poorly characterised bygeneral-purpose forecasting methods. Here we propose an approach to predict thetiming and level of daily peak demand, and a data fusion procedure forcombining conventional and peak forecasts to produce a general-purposeprobabilistic forecast with improved performance during peaks. The proposedapproach is demonstrated using real smart meter data and a hypothetical lowvoltage network hierarchy comprising feeders, secondary and primarysubstations. Fusing state-of-the-art probabilistic load forecasts with peakforecasts is found to improve performance overall, particularly at smart-meterand feeder levels and during peak hours, where improvement in terms of CRPSexceeds 10%.

Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks