@Article{NobreSKKZMPRR:2016:PVPoCo,
author = "Nobre, Andr{\'e} M. and Severiano J{\'u}nior, Carlos A. and
Karthik, Shravan and Kubis, Marek and Zhao, Lu and Martins,
Fernando Ramos and Pereira, Enio Bueno and R{\"u}ther, Ricardo
and Reindl, Thomas",
affiliation = "{National University of Singapore} and {Universidade Federal de
Minas Gerais (UFMG)} and {National University of Singapore} and
{National University of Singapore} and {National University of
Singapore} and {Instituto Nacional de Pesquisas Espaciais (INPE)}
and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Federal de Santa Catarina (UFSC)} and {National
University of Singapor}",
title = "PV power conversion and short-term forecasting in a tropical,
densely-built environment in Singapore",
journal = "Renewable Energy",
year = "2016",
volume = "94",
pages = "496--509",
month = "Aug.",
keywords = "PV power conversion, PV systems, Short-term prediction, Solar
irradiance forecasting, Tropical regions.",
abstract = "With the substantial growth of solar photovoltaic installations
worldwide, forecasting irradiance becomes a critical step in
providing a reliable integration of solar electricity into
electric power grids. In Singapore, the number of PV installation
has increased with a growth rate of 70% over the past 6 years.
Within the next decade, solar power could represent up to 20% of
the instant power generation. Challenges for PV grid integration
in Singapore arise from the high variability in cloud movements
and irradiance patterns due to the tropical climate. For a
thorough analysis and modeling of the impact of an increasing
share of variable PV power on the electric power system, it is
indispensable (i) to have an accurate conversion model from
irradiance to solar power generation, and (ii) to carry out
irradiance forecasting on various time scales. In this work, we
demonstrate how common assumptions and simplifications in PV power
conversion methods negatively affect the output estimates of PV
systems power in a tropical and densely-built environment such as
in Singapore. In the second part, we propose and test a novel
hybrid model for short-term irradiance forecasting for short-term
intervals. The hybrid model outperforms the persistence forecast
and other common statistical methods.",
doi = "10.1016/j.renene.2016.03.075",
url = "http://dx.doi.org/10.1016/j.renene.2016.03.075",
issn = "0960-1481",
language = "en",
targetfile = "nobre_pv.pdf",
urlaccessdate = "27 abr. 2024"
}