Data-Driven Minute-Ahead Forecast of PV Generation with Adjacent PV Sector Information

Jimyung Kang (), Jooseung Lee and Soonwoo Lee
Additional contact information
Jimyung Kang: Korea Electrotechnology Research Institute, Changwon 51543, Republic of Korea
Jooseung Lee: Korea Electrotechnology Research Institute, Changwon 51543, Republic of Korea
Soonwoo Lee: Korea Electrotechnology Research Institute, Changwon 51543, Republic of Korea

Energies, 2023, vol. 16, issue 13, 1-16

Abstract: This paper proposes and validates a data-driven minute-ahead forecast model for photovoltaic (PV) generation, which is essential for real-time micro-grid scheduling. Unlike day-ahead PV forecasts that heavily rely on weather forecast information, our proposed model does not require such data as it operates in an ultra-short-term time domain. Instead, the model leverages the generation data of the target PV sector and its adjacent sectors to capture short-term factors that affect electricity generation, such as the movement of clouds. The proposed model employs a long short-term memory (LSTM) network to process the data. By conducting experiments with real PV site data, we demonstrate that the information from adjacent PV sectors improves the accuracy of minute-ahead PV generation forecasts by 3.66% in the mean squared error index and 1.19% in the mean absolute error index compared to the model without adjacent sector information.

Keywords: minute-ahead PV forecast; adjacent PV sector; LSTM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/13/4905/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/13/4905/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:13:p:4905-:d:1177808

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().