Data-driven solar generation forecast considering temporal characteristics of data

Guangyuan Shi, Sara Eftekharnejad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Compared to traditional generating resources, distributed Photovoltaic (PV) resources are highly intermittent in nature. Hence, ensuring reliability under high penetration of PV systems would require additional reserves, purchase of power from real-time markets, or renewable curtailments, all of which dramatically increase cost of power generation and transmission. Accurate PV forecasting can eliminate the need for these costly measures and is necessary for seamless grid operations with high levels of solar PV systems. PV forecasting, however, is challenged by the fact that PV generation behavior differs in different regions and under different weather patterns. Hence, it is critical to consider both the spatial and temporal characteristics of the data when developing a data-driven forecast model. This paper seeks to provide a comparison of different methods for forecasting solar irradiance, using data from the Syracuse area, with diverse weather patterns that challenge PV forecasting. For a large dataset, a new approach to forecasting based on Symbolic Aggregate Approximation is introduced which enables increasing the calculation speed and reducing the dimensions for accurate forecasting.

Original languageEnglish (US)
Title of host publication2020 IEEE Texas Power and Energy Conference, TPEC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728144368
DOIs
StatePublished - Feb 2020
Event2020 IEEE Texas Power and Energy Conference, TPEC 2020 - College Station, United States
Duration: Feb 6 2020Feb 7 2020

Publication series

Name2020 IEEE Texas Power and Energy Conference, TPEC 2020

Conference

Conference2020 IEEE Texas Power and Energy Conference, TPEC 2020
CountryUnited States
CityCollege Station
Period2/6/202/7/20

Keywords

  • Artificial Neural Networks
  • Photovoltaic Forecasting
  • Renewable Generation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Shi, G., & Eftekharnejad, S. (2020). Data-driven solar generation forecast considering temporal characteristics of data. In 2020 IEEE Texas Power and Energy Conference, TPEC 2020 [9042521] (2020 IEEE Texas Power and Energy Conference, TPEC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TPEC48276.2020.9042521