Presentation | 2013-12-19 Uncertainty Assessment of Solar Radiation Forecasting under Smart Grid Environment Masato TAKAHASIHI, Hiroyuki MORI, |
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Abstract(in English) | This paper proposes a kernel machine method for forecast solar radiation prediction with Gaussian Processes. Recently, renewable energy such as PV systems, wind power generation, etc. is popular in the world. Renewable energy plays an important role to suppress the CO_2 emissions. However, it is difficult to introduce renewable energy into smart grids because renewable energy brings about a large amount of the uncertainties to smart grids operation and planning so that higher accurate model is needed for the forecasting methods. This paper focuses on solar radiation forecasting in PV systems. The proposed method makes use of the hierarchical Bayesian model that expresses the distribution of the predicted value called error bars. The proposed method is successfully applied to real weather data. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Solar Radiation Forecasting / Kernel Machine / Gaussian Processes / Error Bar |
Paper # | SSS2013-28 |
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Committee | SSS |
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Conference Date | 2013/12/12(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Safety (SSS) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Uncertainty Assessment of Solar Radiation Forecasting under Smart Grid Environment |
Sub Title (in English) | |
Keyword(1) | Solar Radiation Forecasting |
Keyword(2) | Kernel Machine |
Keyword(3) | Gaussian Processes |
Keyword(4) | Error Bar |
1st Author's Name | Masato TAKAHASIHI |
1st Author's Affiliation | Dept. of Electronics and Bioinformatics, Faculty of Science and Technology, Meiji University() |
2nd Author's Name | Hiroyuki MORI |
2nd Author's Affiliation | Dept. of Network Design, Faculty of Interdisciplinary Mathematical, Meiji University |
Date | 2013-12-19 |
Paper # | SSS2013-28 |
Volume (vol) | vol.113 |
Number (no) | 362 |
Page | pp.pp.- |
#Pages | 4 |
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