Vol. 8 No. 5 (2020): BUSINESS & MANAGEMENT STUDIES: AN INTERNATIONAL JOURNAL
Articles

INSTALLED SOLAR POWER PREDICTION FOR TURKEY USING ARTIFICIAL NEURAL NETWORK AND BIDIRECTIONAL LONG SHORT-TERM MEMORY

Mehmet Hakan Özdemir, Dr. Öğr. Üyesi
Asisst. Prof. Dr., Turkish-German University
Murat İnce, Dr. Öğr. Üyesi
Asisst. Prof. Dr., Isparta University of Applied Sciences
Batin Latif Aylak, Dr. Öğr. Üyesi
Asisst. Prof. Dr., Turkish-German University
Okan Oral, Dr. Öğr. Üyesi
Asisst. Prof. Dr., Akdeniz University
Mehmet Ali Taş, Arş. Gör.
Res. Asisst., Turkish-German University

Published 2020-12-25

Keywords

  • Renewable Energy,
  • Solar Energy,
  • Prediction,
  • Artificial Neural Network
  • Yenilenebilir Enerji,
  • Güneş Enerjisi,
  • Tahmin,
  • Yapay Sinir Ağları

How to Cite

ÖZDEMİR, M. H., İNCE, M., AYLAK, B. L., ORAL, O., & TAŞ, M. A. (2020). INSTALLED SOLAR POWER PREDICTION FOR TURKEY USING ARTIFICIAL NEURAL NETWORK AND BIDIRECTIONAL LONG SHORT-TERM MEMORY. Business & Management Studies: An International Journal, 8(5), 4047-4068. https://doi.org/10.15295/bmij.v8i5.1639

Abstract

Renewable energy sources play an essential role in sustainable development. The share of renewable energy-based energy generation is rapidly increasing all over the world. Turkey has a great potential in terms of both solar and wind energy due to its geographical location. The desired level has not yet been reached in using this potential. Nevertheless, with the increase in installed power in recent years, electricity generation from solar energy has gained momentum. In this study, data on cumulative installed solar power in Turkey in the 2009-2019 period were used. Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory (BLSTM) methods were selected to predict the cumulative installed solar power for 2020 with these data. The cumulative installed power was predicted, and the results were compared and interpreted.

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