Journal article
Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms
Sustainability, v 15(11), p8927
01 Jun 2023
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Photovoltaic (PV) panels need to be exposed to sufficient solar radiation to produce the desired amount of electrical power. However, due to the stochastic nature of solar irradiance, smooth solar energy harvesting for power generation is challenging. Most of the available literature uses machine learning models trained with data gathered over a single time horizon from a location to forecast solar radiation. This study uses eight machine learning models trained with data gathered at various time horizons over two years in Limpopo, South Africa, to forecast solar irradiance. The goal was to study how the time intervals for forecasting the patterns of solar radiation affect the performance of the models in addition to determining their accuracy. The results of the experiments generally demonstrate that the models' accuracy decreases as the prediction horizons get longer. Predictions were made at 5, 10, 15, 30, and 60 min intervals. In general, the deep learning models outperformed the conventional machine learning models. The Convolutional Long Short-Term Memory (ConvLSTM) model achieved the best Root Mean Square Error (RMSE) of 7.43 at a 5 min interval. The Multilayer Perceptron (MLP) model, however, outperformed other models in most of the prediction intervals.
Metrics
Details
- Title
- Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms
- Creators
- Chibuzor N. Obiora - University of JohannesburgAli N. Hasan - University of JohannesburgAhmed Ali - University of Johannesburg
- Publication Details
- Sustainability, v 15(11), p8927
- Publisher
- Mdpi
- Number of pages
- 17
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Engineering Technology
- Web of Science ID
- WOS:001005364800001
- Scopus ID
- 2-s2.0-85161612024
- Other Identifier
- 991022004199504721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Environmental Sciences
- Environmental Studies
- Green & Sustainable Science & Technology