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Research Papers

Day-Ahead Solar Irradiance Forecasting in a Tropical Environment

[+] Author and Article Information
Aloysius W. Aryaputera

Solar Energy Research Institute of Singapore,
National University of Singapore,
Block E3A, #06-01,
7 Engineering Drive 1,
Singapore 117574, Singapore;
Department of Electrical
and Computer Engineering,
National University of Singapore,
4 Engineering Drive 3,
Singapore 117583, Singapore
e-mail: a0045599@u.nus.edu

Dazhi Yang

Singapore Institute of Manufacturing
Technology (SIMTech),
Agency for Science, Technology
and Research (A*STAR),
71 Nanyang Drive,
Singapore 638075, Singapore

Wilfred M. Walsh

Solar Energy Research Institute of Singapore,
National University of Singapore,
Block E3A, #06-01,
7 Engineering Drive 1,
Singapore 117574, Singapore

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received November 12, 2014; final manuscript received March 23, 2015; published online July 27, 2015. Assoc. Editor: Philippe Blanc.

J. Sol. Energy Eng 137(5), 051009 (Jul 27, 2015) (7 pages) Paper No: SOL-14-1336; doi: 10.1115/1.4030231 History: Received November 12, 2014

Day-ahead solar irradiance forecasting is carried out using data from a tropical environment, Singapore. The performance of the weather research and forecasting (WRF) model is evaluated. We explore various combinations of physics configuration setups in the WRF model and propose a setup for the tropical regions. The WRF model is benchmarked using persistence and two seasonal time series models, namely, the exponential smoothing (ETS) and seasonal autoregressive integrated moving average (SARIMA) models. It is shown that the WRF model outperforms the SARIMA model and achieves accuracies comparable with persistence and ETS models. Persistence, ETS, and WRF models have relative root mean square errors (rRMSE) of about 55–57%. Furthermore, we find that by combining the forecasting outputs of WRF and ETS models, errors can be reduced to 49%.

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Figures

Grahic Jump Location
Fig. 1

Map of Singapore (left) and WRF simulation domain (right)

Grahic Jump Location
Fig. 2

Interactions of WRF parameterizations (adopted from Ref. [30])

Grahic Jump Location
Fig. 3

Comparison between rRMSE and simulation grid resolutions

Grahic Jump Location
Fig. 4

Comparison of annual (selected 60 days) rRMSE values of different forecasting methods in different sky conditions

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