Relevance vector-machine-based solar cell model
| Autor: | Dipankar Bhattacharya, M. Germin Nisha, G. N. Pillai |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Fluid Flow and Transfer Processes
Engineering Artificial neural network Maximum power principle Renewable Energy Sustainability and the Environment business.industry Process Chemistry and Technology Photovoltaic system Sparse approximation Translation (geometry) Plot (graphics) Support vector machine Relevance vector machine General Energy Fuel Technology business Algorithm |
| Zdroj: | International Journal of Sustainable Energy. 34:685-692 |
| ISSN: | 1478-646X 1478-6451 |
| Popis: | This paper proposes an advanced machine learning method, relevance vector machines (RVMs), to model photovoltaic (PV) cells with a few measured data, over a range of expected operating conditions. RVMs are established on a Bayesian formulation which results in usage of less number of relevance vectors leading to much more sparse representation than the support vector machine. The RVM model can be used to predict short-circuit current and open-circuit voltage and thereby maximum power point for any unknown temperature and irradiation. Coordinate translation technique is used to plot the nonlinear I–V characteristics of PV cells. The proposed method matches the measured data more accurately than the pure neural network model and the neuro-fuzzy model. |
| Databáze: | OpenAIRE |
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