Daily Global Solar Radiation Forecasting Using ANN and Extreme Learning Machine: A Case Study in Saudi Arabia
| dc.contributor.author | Alharbi, Maher | |
| dc.contributor.copyright-release | No | en_US |
| dc.contributor.degree | Master of Applied Science | en_US |
| dc.contributor.department | Department of Electrical & Computer Engineering | en_US |
| dc.contributor.ethics-approval | Not Applicable | en_US |
| dc.contributor.external-examiner | Dr.W.Phillips | en_US |
| dc.contributor.graduate-coordinator | Dr.llow.J | en_US |
| dc.contributor.manuscripts | No | en_US |
| dc.contributor.thesis-reader | Dr.J.Gu | en_US |
| dc.contributor.thesis-supervisor | Dr.M.El-Hawary | en_US |
| dc.date.accessioned | 2013-03-14T14:47:04Z | |
| dc.date.available | 2013-03-14T14:47:04Z | |
| dc.date.defence | 2013-03-07 | |
| dc.date.issued | 2013-03-14 | |
| dc.description | It is a comperison between ANN and ELM | en_US |
| dc.description.abstract | 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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10222/21401 | |
| dc.language.iso | en | en_US |
| dc.subject | Global solar radiation forecasting | en_US |
| dc.title | Daily Global Solar Radiation Forecasting Using ANN and Extreme Learning Machine: A Case Study in Saudi Arabia | en_US |