Fuzzy prediction interval models for forecasting renewable resources and loads in microgrids

Doris Sáez, Fernand Ávila, Daniel Olivares, Claudio Cañizares, Luis Marín

Research output: Contribution to journalArticlepeer-review

139 Scopus citations

Abstract

An energy management system (EMS) determines the dispatching of generation units based on an optimizer that requires the forecasting of both renewable resources and loads. The forecasting system discussed in this paper includes a representation of the uncertainties associated with renewable resources and loads. The proposed modeling generates fuzzy prediction interval models that incorporate an uncertainty representation of future predictions. The model is demonstrated using solar and wind generation and local load data from a real microgrid in Huatacondo, Chile, for one-day ahead forecasts to obtain the expected values together with fuzzy prediction intervals to represent future measurement bounds with a certain coverage probability. The proposed prediction interval models would help to enable the development of robust microgrid EMS.

Original languageEnglish
Article number6994295
Pages (from-to)548-556
Number of pages9
JournalIEEE Transactions on Smart Grid
Volume6
Issue number2
DOIs
StatePublished - 1 Mar 2015
Externally publishedYes

Keywords

  • Energy management system (EMS)
  • forecasting
  • fuzzy modeling
  • microgrid
  • prediction intervals
  • renewable

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