Open AccessWeather forecasting error in solar energy forecasting
Author(s) -
Sangrody Hossein,
Sarailoo Morteza,
Zhou Ning,
Tran Nhu,
Motalleb Mahdi,
Foruzan Elham
Publication year - 2017
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2016.1043
Subject(s) - probabilistic forecasting , photovoltaic system , renewable energy , meteorology , weather forecasting , weather station , solar energy , model output statistics , north american mesoscale model , numerical weather prediction , global forecast system , computer science , environmental science , engineering , artificial intelligence , geography , probabilistic logic , electrical engineering
As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model while in practice the energy forecasting is based on forecasted weather data. A study on the uncertainty in weather forecasting for the most commonly used weather variables is presented. The forecasted weather data for 6 days ahead is compared with the observed data and the results of analysis are quantified by statistical metrics. In addition, the most influential weather predictors in energy forecasting model are selected. The performance of historical and observed weather data errors is assessed using a solar PV generation forecasting model. Finally, a sensitivity test is performed to identify the influential weather variables whose accurate values can significantly improve the results of energy forecasting.