A Review of Data Mining and Solar Power Prediction


YEŞİLBUDAK M., Colak M., BAYINDIR R.

5th IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, İngiltere, 20 - 23 Kasım 2016, ss.1117-1121 identifier identifier

  • Cilt numarası:
  • Doi Numarası: 10.1109/icrera.2016.7884507
  • Basıldığı Şehir: Birmingham
  • Basıldığı Ülke: İngiltere
  • Sayfa Sayıları: ss.1117-1121

Özet

Solar energy is one of the clean and renewable energy sources that are mostly available in the world. As a result of this situation, there are many research studies done on the solar energy in order to get the maximum solar radiation during the day time, to estimate the solar power generation and to increase the efficiency of solar systems. In this paper, especially, a review of data mining methods employed for solar power prediction in the literature is introduced briefly. Input data, recording intervals, the number of training and test datasets of each study are also considered in the review process. It is shown that artificial neural networks are the most preferred methods in order to predict solar power generation.