Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting
Solar irradiance forecasting is an inevitable and most significant process in grid-connected photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid operation efficiently, with irradiance forecasting for various timescales, such as an hour ahead, a day ahead, and a week a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/9/2404 |
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| author | N. Yogambal Jayalakshmi R. Shankar Umashankar Subramaniam I. Baranilingesan Alagar Karthick Balasubramaniam Stalin Robbi Rahim Aritra Ghosh |
| author_facet | N. Yogambal Jayalakshmi R. Shankar Umashankar Subramaniam I. Baranilingesan Alagar Karthick Balasubramaniam Stalin Robbi Rahim Aritra Ghosh |
| author_sort | N. Yogambal Jayalakshmi |
| collection | DOAJ |
| description | Solar irradiance forecasting is an inevitable and most significant process in grid-connected photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid operation efficiently, with irradiance forecasting for various timescales, such as an hour ahead, a day ahead, and a week ahead, strategies are developed and analysed in this article. However, the single time scale model can perform better for that specific time scale but cannot be employed for other time scale forecasting. Moreover, the data consideration for single time scale forecasting is limited. In this work, a multi-time scale model for solar irradiance forecasting is proposed based on the multi-task learning algorithm. An effective resource sharing scheme between each task is presented. The proposed multi-task learning algorithm is implemented with a long short-term memory (LSTM) neural network model and the performance is investigated for various time scale forecasting. The hyperparameter estimation of the proposed LSTM model is made by a hybrid chicken swarm optimizer based on combining the best features of both the chicken swarm optimization algorithm (CSO) and grey wolf optimization (GWO) algorithm. The proposed model is validated, comparing existing methodologies for single timescale forecasting, and the proposed strategy demonstrated highly consistent performance for all time scale forecasting with improved metric results. |
| first_indexed | 2024-03-10T12:03:13Z |
| format | Article |
| id | doaj.art-d0f4c0c468b448ba9d6ddd69d1b93081 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T12:03:13Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d0f4c0c468b448ba9d6ddd69d1b930812023-11-21T16:49:58ZengMDPI AGEnergies1996-10732021-04-01149240410.3390/en14092404Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance ForecastingN. Yogambal Jayalakshmi0R. Shankar1Umashankar Subramaniam2I. Baranilingesan3Alagar Karthick4Balasubramaniam Stalin5Robbi Rahim6Aritra Ghosh7Department of Electrical and Electronics Engineering, Dr. Mahalingam College of Engineering and Technology, Coimbatore 642003, IndiaDepartment of Electronics and Communication Engineering, Teegala Krishna Reddy Engineering College, Hyderabad 500097, IndiaDepartment of Communications and Networks, Renewable Energy Lab, College of Engineering, Prince Sultan University Riyadh, Riyadh 12435, Saudi ArabiaDepartment of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Arasur Coimbatore 641047, IndiaDepartment of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Arasur Coimbatore 641047, IndiaDepartment of Mechanical Engineering, Regional Campus Madurai, Anna University, Madurai 625019, IndiaDepartment of Informatics Management, Sekolah Tinggi Ilmu Manajemen Sukma, Medan, Sumatera Utara 20219, IndonesiaCollege of Engineering, Mathematics and Physical Sciences, Renewable Energy, University of Exeter, Cornwall TR10 9FE, UKSolar irradiance forecasting is an inevitable and most significant process in grid-connected photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid operation efficiently, with irradiance forecasting for various timescales, such as an hour ahead, a day ahead, and a week ahead, strategies are developed and analysed in this article. However, the single time scale model can perform better for that specific time scale but cannot be employed for other time scale forecasting. Moreover, the data consideration for single time scale forecasting is limited. In this work, a multi-time scale model for solar irradiance forecasting is proposed based on the multi-task learning algorithm. An effective resource sharing scheme between each task is presented. The proposed multi-task learning algorithm is implemented with a long short-term memory (LSTM) neural network model and the performance is investigated for various time scale forecasting. The hyperparameter estimation of the proposed LSTM model is made by a hybrid chicken swarm optimizer based on combining the best features of both the chicken swarm optimization algorithm (CSO) and grey wolf optimization (GWO) algorithm. The proposed model is validated, comparing existing methodologies for single timescale forecasting, and the proposed strategy demonstrated highly consistent performance for all time scale forecasting with improved metric results.https://www.mdpi.com/1996-1073/14/9/2404solar irradiance forecastingmulti-task learningmulti-time scale predictionLSTMhybrid CSO-GWO |
| spellingShingle | N. Yogambal Jayalakshmi R. Shankar Umashankar Subramaniam I. Baranilingesan Alagar Karthick Balasubramaniam Stalin Robbi Rahim Aritra Ghosh Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting Energies solar irradiance forecasting multi-task learning multi-time scale prediction LSTM hybrid CSO-GWO |
| title | Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting |
| title_full | Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting |
| title_fullStr | Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting |
| title_full_unstemmed | Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting |
| title_short | Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting |
| title_sort | novel multi time scale deep learning algorithm for solar irradiance forecasting |
| topic | solar irradiance forecasting multi-task learning multi-time scale prediction LSTM hybrid CSO-GWO |
| url | https://www.mdpi.com/1996-1073/14/9/2404 |
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