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

Ciaran Gilbert, Jethro Browell, Bruce Stephen

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
55 Downloads (Pure)

Abstract

Short-term forecasts of energy consumption are invaluable for operation of energy systems, including low voltage electricity networks. However, network loads are challenging to predict when highly desegregated to small numbers of customers, which may be dominated by individual behaviours rather than the smooth profiles associated with aggregate consumption. Furthermore, distribution networks are challenged almost entirely by peak loads, and tasks such as scheduling storage and/or demand flexibility maybe be driven by predicted peak demand, a feature that is often poorly characterised by general-purpose forecasting methods. Here we propose an approach to predict the timing and level of daily peak demand, and a data fusion procedure for combining conventional and peak forecasts to produce a general-purpose probabilistic forecast with improved performance during peaks. The proposed approach is demonstrated using real smart meter data and a hypothetical low voltage network hierarchy comprising feeders, secondary and primary substations. Fusing state-of-the-art probabilistic load forecasts with peak forecasts is found to improve performance overall, particularly at smart-meter and feeder levels and during peak hours, where improvement in terms of CRPS exceeds 10%.
Original languageEnglish
Article number100998
Number of pages12
JournalSustainable Energy, Grids and Networks
Volume34
Early online date18 Jan 2023
DOIs
Publication statusPublished - 30 Jun 2023

Keywords

  • low voltage
  • load forecasting
  • demand forecasting
  • smart meters
  • probabilistic forecasting
  • forecasting combination

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