Spatial estimation of solar radiation using geostatistics and machine learning techniques
| Autor: | A. Núñez-Reyes, S. Ruiz-Moreno |
|---|---|
| Přispěvatelé: | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
020901 industrial engineering & automation Spatial estimation Control and Systems Engineering Computer science 020208 electrical & electronic engineering Machine learning 0202 electrical engineering electronic engineering information engineering Distributed control and Estimation 02 engineering and technology Geostatistics Sensors networks Remote sensing |
| Zdroj: | idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| Popis: | Cuenta con un 2º editor: IFAC-PapersOnLine Incluido en el Volumen 53, Nº 2 Article number 145388 In large solar fields, where the control system is distributed, it is important to know the values of solar radiation in the complete area. Local solar radiation can be obtained by means of static sensors, using e.g. a wireless sensor network or movable sensors with drones for the general obtainment of variables. In this paper, solar radiation estimation is accomplished using Ordinary Kriging and distance weighting, and an alternative method is presented, which is based on a non-supervised competitive artificial neural network called Self-Organizing Map. This neural network generates a map with the most representative nodes and their weights, which are used to obtain the spatial variability of solar radiation in the area. |
| Databáze: | OpenAIRE |
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