Conference proceeding
Estimation of Hourly Global Solar Radiation Using Deep Learning Algorithms
International Renewable Energy Congress (Online), pp 1-6
01 Jan 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Lately, the environmental impacts of fossil energy have forced many countries to begin advocating for renewable energy sources (RES). Solar energy is one of the RES that has gained worldwide acceptability because it is cheap and available. However, solar power is characteristically unpredictable. Accurate prediction of global solar radiation (GSR) continues to be a great need both in the field of physical simulations and artificial intelligence. Both statistical and machine learning methods have proved to be very useful, but researchers are still working on algorithms that can give the least possible error. In this paper, four deep learning algorithms were used to successfully to estimate GSR. Historical meteorological data for Johannesburg city were used as the dataset. The dataset was split into training and testing data. Results obtained showed that Convolutional Long Short-Term Memory (ConvLSTM) with nRMSE value of 1.62% performed better than the other models. Other models used were convolutional neural network (CNN), Long Short-Term Memory Network (LSTM), and a hybrid of CNN and LSTM known as CNN-LSTM network. Real-time implementation of these results to solar plant operations in South Africa could go long to generate a stable electric power for the smart grid.
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Details
- Title
- Estimation of Hourly Global Solar Radiation Using Deep Learning Algorithms
- Creators
- Chibuzor N. Obiora - University of JohannesburgAhmed Ali - University of JohannesburgAli N. Hasan - Higher Colleges of Technology
- Publication Details
- International Renewable Energy Congress (Online), pp 1-6
- Series
- International Renewable Energy Congress
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Web of Science ID
- WOS:000652593700080
- Scopus ID
- 2-s2.0-85100153307
- Other Identifier
- 991022004619304721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Energy & Fuels