Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network
| Autor: | Mohammed Aggour, Merouan Belkasmi, María del Mar Castilla, Jose Domingo Alvarez Hervas, Reda Yaagoubi, Manuel Pérez García, Khalid Bouziane, Mensah K. Anaty, Yaser I. Alamin |
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| Rok vydání: | 2020 |
| Předmět: |
Control and Optimization
Computer science 020209 energy Energy Engineering and Power Technology RBF 02 engineering and technology lcsh:Technology Electric power system 0202 electrical engineering electronic engineering information engineering Production (economics) Concentrator photovoltaic Electrical and Electronic Engineering Engineering (miscellaneous) Artificial neural network lcsh:T Renewable Energy Sustainability and the Environment business.industry 021001 nanoscience & nanotechnology Grid power prediction Term (time) Renewable energy Reliability engineering Power (physics) HCPV ANN 0210 nano-technology business Energy (signal processing) Energy (miscellaneous) |
| Zdroj: | riUAL. Repositorio Institucional de la Universidad de Almería Universidad de Almería Energies Volume 13 Issue 13 Pages: 3493 Energies, Vol 13, Iss 3493, p 3493 (2020) |
| ISSN: | 1996-1073 |
| Popis: | Concentrator photovoltaic (CPV) is used to obtain cheaper and more stable renewable energy. Methods which predict the energy production of a power system under specific circumstances are highly important to reach the goal of using this system as a part of a bigger one or of making it integrated with the grid. In this paper, the development of a model to predict the energy of a High CPV (HCPV) system using an Artificial Neural Network (ANN) is described. This system is located at the University of Rabat. The performed experiments show a quick prediction with encouraging results for a very short-term prediction horizon, considering the small amount of data available. These conclusions are based on the processes of obtaining the ANN models and detailed discussion of the results, which have been validated using real data. |
| Databáze: | OpenAIRE |
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