| contributor author | Cornaro, C. | |
| contributor author | Bucci, F. | |
| contributor author | Pierro, M. | |
| contributor author | Del Frate, F. | |
| contributor author | Peronaci, S. | |
| contributor author | Taravat, A. | |
| date accessioned | 2017-05-09T01:23:28Z | |
| date available | 2017-05-09T01:23:28Z | |
| date issued | 2015 | |
| identifier issn | 0199-6231 | |
| identifier other | sol_137_03_031011.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/159607 | |
| description abstract | In this paper, several models to forecast the hourly solar irradiance with a day in advance using artificial neural network techniques have been developed and analyzed. The forecast irradiance is the one incident on the plane of the modules array of a photovoltaic plant. Pure statistical (ST) models that use only local measured data and model output statistics (MOS) approaches to refine numerical weather prediction data are tested for the University of Rome “Tor Vergata†site. The performance of ST and MOS, together with the persistence model (PM), is compared. The ST models improve the performance of the PM of around 20%. The combination of ST and NWP in the MOS approach gives the best performance, improving the forecast of approximately 39% with respect to the PM. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Twenty Four Hour Solar Irradiance Forecast Based on Neural Networks and Numerical Weather Prediction | |
| type | Journal Paper | |
| journal volume | 137 | |
| journal issue | 3 | |
| journal title | Journal of Solar Energy Engineering | |
| identifier doi | 10.1115/1.4029452 | |
| journal fristpage | 31011 | |
| journal lastpage | 31011 | |
| identifier eissn | 1528-8986 | |
| tree | Journal of Solar Energy Engineering:;2015:;volume( 137 ):;issue: 003 | |
| contenttype | Fulltext | |
