BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/6/2226 |
| _version_ | 1797446610199773184 |
|---|---|
| author | Mohammed A. Bou-Rabee Muhammad Yasin Naz Imad ED. Albalaa Shaharin Anwar Sulaiman |
| author_facet | Mohammed A. Bou-Rabee Muhammad Yasin Naz Imad ED. Albalaa Shaharin Anwar Sulaiman |
| author_sort | Mohammed A. Bou-Rabee |
| collection | DOAJ |
| description | 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 irradiance 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. |
| first_indexed | 2024-03-09T13:43:05Z |
| format | Article |
| id | doaj.art-39f8d0f12b3d437cb22c26041f123bd3 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T13:43:05Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-39f8d0f12b3d437cb22c26041f123bd32023-11-30T21:03:50ZengMDPI AGEnergies1996-10732022-03-01156222610.3390/en15062226BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate ZonesMohammed A. Bou-Rabee0Muhammad Yasin Naz1Imad ED. Albalaa2Shaharin Anwar Sulaiman3Department of Electrical Engineering, College of Technical Studies, PAAET, Safat 13092, KuwaitDepartment of Physics, Plasma and Flow Assurance Lab., University of Agriculture, Faisalabad 38040, PakistanDepartment of Science, College-Basic Education, PAAET, Safat 22081, KuwaitDepartment of Mechanical Engineering, Universiti Teknologi Petronas, Persiaran UTP, Seri Iskandar 32610, Perak, MalaysiaRecent 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 irradiance 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.https://www.mdpi.com/1996-1073/15/6/2226solar radiation predictionwavelet decompositioncoevolutionary neural networkattention-based bidirectional long short-term memory |
| spellingShingle | Mohammed A. Bou-Rabee Muhammad Yasin Naz Imad ED. Albalaa Shaharin Anwar Sulaiman BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones Energies solar radiation prediction wavelet decomposition coevolutionary neural network attention-based bidirectional long short-term memory |
| title | BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones |
| title_full | BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones |
| title_fullStr | BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones |
| title_full_unstemmed | BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones |
| title_short | BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones |
| title_sort | bilstm network based approach for solar irradiance forecasting in continental climate zones |
| topic | solar radiation prediction wavelet decomposition coevolutionary neural network attention-based bidirectional long short-term memory |
| url | https://www.mdpi.com/1996-1073/15/6/2226 |
| work_keys_str_mv | AT mohammedabourabee bilstmnetworkbasedapproachforsolarirradianceforecastingincontinentalclimatezones AT muhammadyasinnaz bilstmnetworkbasedapproachforsolarirradianceforecastingincontinentalclimatezones AT imadedalbalaa bilstmnetworkbasedapproachforsolarirradianceforecastingincontinentalclimatezones AT shaharinanwarsulaiman bilstmnetworkbasedapproachforsolarirradianceforecastingincontinentalclimatezones |