Conference proceeding
Forecasting Hourly Solar Irradiance Using Long Short-Term Memory (LSTM) Network
International Renewable Energy Congress (Online), pp 1-6
01 Jan 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The increasing global demand for solar energy is a good indicator that it is a viable alternative to fossil energy. However, solar irradiance which is the principal component required for efficient power generation in solar plants is stochastic in nature. This is the reason why the prediction accuracy of solar irradiance for reliable electricity power output continues to be a very difficult task whether in the field of artificial intelligence (AI) or physical simulation. In this paper, Long Short-Term Memory (LSTM) Network, a variant of Recurrent Neural Network (RNN), was used to forecast hourly solar irradiance of Johannesburg city. LSTM is a deep learning network designed to overcome the vanishing and exploding gradient problems associated with a typical RNN. Ten years of historical meteorological data used in this work were obtained from Meteoblue. The simulation results obtained using the LSTM model were compared with the ones recorded using Support Vector Regression (SVR). From the results, it was observed that the LSTM network with normalized Root Mean Square Error (nRMSE) value of 3.2% performed much better than SVR using the same dataset.
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Details
- Title
- Forecasting Hourly Solar Irradiance Using Long Short-Term Memory (LSTM) Network
- Creators
- Chibuzor N. Obiora - University of JohannesburgAhmed Ali - University of JohannesburgAli N. Hasan - University of Johannesburg
- 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:000652593700077
- Scopus ID
- 2-s2.0-85100165078
- Other Identifier
- 991022004637804721
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- Web of Science research areas
- Energy & Fuels