Application of extreme learning machine for estimating solar radiation from satellite data


Sahin M., Kaya Y., Uyar M., Yildirim S.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, vol.38, no.2, pp.205-212, 2014 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 2
  • Publication Date: 2014
  • Doi Number: 10.1002/er.3030
  • Journal Name: INTERNATIONAL JOURNAL OF ENERGY RESEARCH
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.205-212
  • Keywords: solar radiation, extreme learning machine, remote sensing, satellite data, NOAA, ARTIFICIAL NEURAL-NETWORK, LAND-SURFACE-TEMPERATURE, TURKEY

Abstract

In this paper, a simple and fast method based on extreme learning machine (ELM) for the estimation of solar radiation in Turkey was presented. To design the ELM model, satellite data of the National Oceanic and Atmospheric Administration advanced very high-resolution radiometer from 20 locations spread over Turkey were used. The satellite-based land surface temperature, altitude, latitude, longitude, month, and city were applied as input to the ELM, and the output variable is the solar radiation. To show the applicability of the ELM model, a performance comparison in terms of the estimation capability and the learning speed was made between the ELM model and conventional artificial neural network (ANN) model with backpropagation. The comparison results showed that the ELM model gave better estimation than the ANN model for the overall test locations. Moreover, the ELM model was about 23.5 times faster than the ANN model. The method could be used by researchers or scientists to design high-efficiency solar devices such as solar power plant and photovoltaic cell. Copyright (c) 2013 John Wiley & Sons, Ltd.