A guide to solar power forecasting using ARMA models

Singh, Bismark and Pozo, David (2019) A guide to solar power forecasting using ARMA models. In Proceedings of 2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2019. IEEE.. (doi:10.1109/ISGTEurope.2019.8905430).

Record type: Conference or Workshop Item (Paper)

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.

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Published date: 29 September 2019

Additional Information: Funding Information: ACKNOWLEDGMENTS Bismark Singh thanks Jean-Paul Watson and Andrea Staid for helpful discussions and for sharing data. His work was supported in part by Sandia’s Laboratory Directed Research and Development (LDRD) program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. David Pozo’s work was supported by Skoltech NGP Program (Skoltech-MIT joint project). SAND2019-6671 J. Publisher Copyright: © 2019 IEEE.

Venue - Dates: 2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2019, , Bucharest, Romania, 2019-09-29 - 2019-10-02

Keywords: ARMA, forecasting, photovoltaic, scenario generation, solar power