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Prediction of diffuse horizontal irradiance using a new climate zone model
Knowledge on the diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI) is crucial for the estimation of the irradiance on tilted surfaces, which in turn is critical for photovoltaic (PV) applications and for designing and simulating concentrated solar power (CSP) plants. Since glob...
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Published in: | Renewable & sustainable energy reviews 2019-08, Vol.110 (C), p.28-42 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Knowledge on the diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI) is crucial for the estimation of the irradiance on tilted surfaces, which in turn is critical for photovoltaic (PV) applications and for designing and simulating concentrated solar power (CSP) plants. Since global horizontal irradiance (GHI) is the most commonly measured solar radiation variable, it is advantageous for establishing a suitable method that uses it to compute DHI and DNI. In this way, a new model for predicting the diffuse fraction (Kd) based on the climate zone is proposed, using only the clearness index (Kt) as the predictor and 1-min resolution GHI data. A review of the literature on models that use hourly and sub-hourly Kt values to compute Kd was also carried out, and an extensive performance assessment of both the proposed model and the models from the literature was conducted using ten statistical indicators and a global performance index (GPI). A set of model parameters was determined for each climate zone considered in this study (arid, high albedo, temperate and tropical) using 48 worldwide radiometric stations. It was found that the best overall performing model was the model proposed in this work.
•Development of climate zone model to determine 1-min diffuse horizontal irradiance data.•Review and statistical analysis of 121 models from the literature.•Use of 1-min data from 48 stations according climate zone.•Best performing models identified according to the climate zone. |
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ISSN: | 1364-0321 1879-0690 |
DOI: | 10.1016/j.rser.2019.04.055 |