Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data

Vahid Nourani, Elnaz Sharghi, Nazanin Behfar and Yongqiang Zhang

Applied Energy, 2022, vol. 315, issue C, No S0306261922004627

Abstract: In this paper two enhanced long-short-term memory (LSTM) models of sequenced-LSTM (SLSTM) and wavelet-LSTM (WLSTM), provided for multi-step-ahead simulation of solar irradiance of six stations, located in Iran and USA. In this respect, twenty-year recorded solar irradiance and climate data were employed. The proposed multi-frequency models serve all the capabilities of classic LSTM network and also handle its weakness in detecting and modeling multi-frequency information that often included in natural datasets. The suggested methodology improved the long-short auto-regressive term of climate-solar irradiance data by including very long frequencies of time series. The results revealed that the suggested multi-frequency LSTM methods could exceed the feed forward neural network and classic LSTM network in test phase up to 23% and 13%, respectively.

Keywords: Solar Irradiance Prediction; LSTM Model; Multi-Frequency Analysis; Climate Data (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922004627
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:315:y:2022:i:c:s0306261922004627

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.2022.119069

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 ().