A Guide to Solar Power Forecasting using ARMA Models

Bismark Singh, David Pozo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Citations (Scopus)

Abstract

In this short article, we summarize a step-by-step methodology to forecast power output from a photovoltaic solar generator using hourly auto-regressive moving average (ARMA) models. We illustrate how to build an ARMA model, to use statistical tests to validate it, and construct hourly samples. The resulting model inherits nice properties for embedding it into more sophisticated operation and planning models, while at the same time showing relatively good accuracy. Additionally, it represents a good forecasting tool for sample generation for stochastic energy optimization models.

Original languageEnglish
Title of host publicationProceedings of 2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538682180
DOIs
Publication statusPublished - Sep 2019
Event2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2019 - Bucharest, Romania
Duration: 29 Sep 20192 Oct 2019

Publication series

NameProceedings of 2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2019

Conference

Conference2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2019
Country/TerritoryRomania
CityBucharest
Period29/09/192/10/19

Keywords

  • ARMA
  • forecasting
  • photovoltaic
  • scenario generation
  • solar power

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