Application of wavelet and seasonal-based emotional ANN (EANN) models to predict solar irradiance

Vahid Nourani; Nazanin Behfar; Anne Ng; Chunwei Zhang; Fahreddin Sadikoglu

doi:10.1016/j.egyr.2024.09.011

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Application of wavelet and seasonal-based emotional ANN (EANN) models to predict solar irradiance

Journal article

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Application of wavelet and seasonal-based emotional ANN (EANN) models to predict solar irradiance

Vahid Nourani, Nazanin Behfar, Anne Ng, Chunwei Zhang and Fahreddin Sadikoglu

Energy Reports, Vol.12, pp.3258-3277

2024

DOI: https://doi.org/10.1016/j.egyr.2024.09.011

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Abstract

Emotional artificial neural network (EANN)

Iran

Seasonal model

Solar irradiance modeling

United States

Wavelet transform

This study models solar irradiance at six stations in Iran and the USA on an hourly scale. We explored two seasonal emotional artificial neural networks (EANN): sequence-EANN (SEANN) and wavelet EANN (WEANN). Analyzing ten years of climatic and solar data, we evaluated uncertainty using prediction intervals (PIs) computed via the bootstrap method based on artificial neural networks (ANNs). Unlike standalone EANNs, the proposed seasonal models effectively captured seasonal information and leveraged time series processing advantages. Utilizing Wavelet and Fourier transforms, these models captured long-short autoregressive dependencies in solar irradiance, addressing extended seasonal dependencies. Results showed that the seasonal EANN models outperformed the classic EANN model by approximately 15 % and the classic feed-forward neural network (FFNN) by about 25 % in both training and testing. The WEANN model demonstrated the highest performance in PIs, with an average normalized mean PI width (NMPIW) of 0.8 and an average PI coverage probability (PICP) of 0.96.

Details

Title: Application of wavelet and seasonal-based emotional ANN (EANN) models to predict solar irradiance
Authors: Vahid Nourani - University of Tabriz
Nazanin Behfar (Corresponding Author) - University of Twente
Anne Ng - Charles Darwin University
Chunwei Zhang - Shenyang University of Technology
Fahreddin Sadikoglu - Odlar Yurdu University
Publication details: Energy Reports, Vol.12, pp.3258-3277
Publisher: Elsevier BV
Date published: 2024
DOI: 10.1016/j.egyr.2024.09.011
ISSN: 2352-4847
Data Availability: Data will be made available on request.
Organisation Unit: School of Science, Technology and Engineering
Language: English
Record Identifier: 991235801702621
Output Type: Journal article

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