The demand for solar radiation forecasting has become a significant feature in the design of photovoltaic (PV) systems. Currently, the artificial neural network (ANN) is the most popular model that is used to estimate solar radiation. However, a new approach, called the extreme learning machine (ELM) algorithm, has been introduced by Huang et al. In this research, ELM and a multilayer feed-forward network with back propagation were used to predict daily global solar radiation. Metrological parameters such as air temperature, humidity and date code have been used as inputs for the ANN and ELM models. The accuracy and performance of these techniques were evaluated by comparing their outputs. ELM is faster than ANN, and results in a high generalization capability. / It is a comperison between ANN and ELM
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/21401 |
| Date | 07 March 2013 |
| Creators | Alharbi, Maher |
| 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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