eprintid: 16996
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/69/96
datestamp: 2023-12-19 03:23:28
lastmod: 2023-12-19 03:23:28
status_changed: 2023-12-19 03:07:16
type: article
metadata_visibility: show
creators_name: Bourabee, M.A.
creators_name: Naz, M.Y.
creators_name: Albalaa, I.E.D.
creators_name: Sulaiman, S.A.
title: BiLSTM Network�Based Approach for Solar Irradiance Forecasting in Continental Climate Zones
ispublished: pub
keywords: Climate models; Deep learning; Forecasting; Solar radiation, Attention�based bidirectional long short�term memory; Cloudy days; Co-evolutionary; Coevolutionary neural network; Continental climate; Neural-networks; Solar irradiances; Solar radiation predictions; Sunny days; Wavelets decomposition, Wavelet decomposition
note: cited By 6
abstract: Recent research on solar irradiance forecasting has attracted considerable attention, as governments worldwide are displaying a keenness to harness green energy. The goal of this study is to build forecasting methods using deep learning (DL) approach to estimate daily solar irradiance in three sites in Kuwait over 12 years (2008�2020). Solar irradiance data are used to extract and understand the symmetrical hidden data pattern and correlations, which are then used to predict future solar irradiance. A DL model based on the attention mechanism applied to bidirectional long short�term memory (BiLSTM) is developed for accurate solar irradiation forecasting. The proposed model is designed for two different conditions (sunny and cloudy days) to ensure greater accuracy in different weather scenarios. Simulation results are presented which depict that the attention based BiLSTM model outperforms the other deep learning networks in the prediction analysis of solar irradiance. The attention based BiLSTM model was able to predict variations in solar irradi-ance over short intervals in continental climate zones (Kuwait) more efficiently with an RMSE of 4.24 and 20.95 for sunny and cloudy days, respectively. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127093704&doi=10.3390%2fen15062226&partnerID=40&md5=6a147a87876576ce1949de600429c4b7
id_number: 10.3390/en15062226
full_text_status: none
publication: Energies
volume: 15
number: 6
refereed: TRUE
issn: 19961073
citation:   Bourabee, M.A. and Naz, M.Y. and Albalaa, I.E.D. and Sulaiman, S.A.  (2022) BiLSTM Network�Based Approach for Solar Irradiance Forecasting in Continental Climate Zones.  Energies, 15 (6).   ISSN 19961073