Decaying fossil fuel resources, international relation complexities, and the risks associated with nuclear power have led to an increased demand for alternative energy sources. Renewable energy sources offer adequate solutions to these challenges.
Forecasting of solar energy has also increased over the past decade due to its use in photovoltaic (PV) system design, load balance in hybrid systems, and projected potential future PV system feasibility. Artificial neural networks (ANN) have been used successfully for solar energy forecasting. In this work, several meteorological variables from Saudi Arabia as a case study will be used to determine the most effective variables on Global Solar Radiation (GSR) prediction. Those variables will be used as inputs for a proposed GSR prediction model. This model will be applicable in different locations and conditions. This model has a simple structure and offers better results in terms of error between actual and predicted solar radiation values.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/50646 |
| Date | 11 June 2014 |
| Creators | Alluhaidah, Bader |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
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