Short-term peer-to-peer solar forecasting in a network of photovoltaic systems

Boudewijn Elsinga and Wilfried G.J.H.M. van Sark

Applied Energy, 2017, vol. 206, issue C, 1464-1483

Abstract: Solar forecasting is a necessary component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents a short term, intra-hour solar forecasting method. This “peer-to-peer” (P2P) forecasting method is based on the cross-correlation time lag between clear-sky index time series of pairs of PV-systems that are influenced by the (assumed) same cloud sequentially, with the feature that the forecast horizon (FH) can be set at a fixed value. The P2P forecasting algorithm was evaluated for 11 central PV-systems (out of 202) over a half year period from the 1st of March through the 31st of August 2015 using the forecast skill (FS) metric. Positive FS means improvement over reference clear-sky index persistence forecasting. The P2P forecasting method was evaluated over a subset of days with either high, all or low irradiance variability. The average forecast skill (avgFS) concerning forecast horizons between 5 and 8min was 5.99%, −1.61% and −16.0% over these periods respectively, indicating the superior performance of the P2P method over persistence during the highly variable days, which are most interesting from the perspective of electricity grid management.

Keywords: Solar forecasting; Intra-hour; Sensor network; Time lag correlation; Irradiance variability (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261917314010
Full text for ScienceDirect subscribers only

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:eee:appene:v:206:y:2017:i:c:p:1464-1483

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2017.09.115

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().