A New Method for Generating Short-Term Power Forecasting Based on Artificial Neural Networks and Optimization Methods for Solar Photovoltaic Power Plants

  • Tugce DemirdelenEmail author
  • Inayet Ozge Aksu
  • Burak Esenboga
  • Kemal Aygul
  • Firat Ekinci
  • Mehmet Bilgili
Part of the Power Systems book series (POWSYS)


In recent times, solar PV power plants have been used worldwide due to their high solar energy potential. Although the PV power plants are highly preferred, the main disadvantage of the system is that the output power characteristics of the system are unstable. As PV power plant system is connected to the grid side, unbalanced power flow effects all systems controls. In addition, the load capacitys is not exactly known. For this reason, it has become an important issue to be known correctly in PV output power and their time-dependent changes. The main aim of this work is to eliminate power plant instability due to the output power imbalance. For the short-term, power prediction is estimated by real-time data of 1 MW PV power plant in use. Estimation power data are compared with real-time data and precision of the proposed method is demonstrated. In the first phase, traditional artificial intelligence algorithms are used. Then, these algorithms are trained with swarm based optimization methods and the performance analyses are presented in detail. Among all the algorithms used, the algorithm with the lowest error is determined. Thus, this study provides useful information and techniques to help researchers who are interested in planning and modeling PV power plants.


Neural networks Optimization methods Short-term Power prediction Photovoltaic plants Firefly algorithm 



The authors would like to acknowledge the Scientific Project Unit of Adana Science and Technology University (Project Number: 18103015 and 18103016) for full financial support.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Tugce Demirdelen
    • 1
    Email author
  • Inayet Ozge Aksu
    • 2
  • Burak Esenboga
    • 1
  • Kemal Aygul
    • 3
  • Firat Ekinci
    • 4
  • Mehmet Bilgili
    • 5
  1. 1.Electrical and Electronics Engineering DepartmentAdana Science and Technology UniversityAdanaTurkey
  2. 2.Computer Engineering DepartmentAdana Science and Technology UniversityAdanaTurkey
  3. 3.Electrical and Electronics Engineering DepartmentCukurova UniversityAdanaTurkey
  4. 4.Energy Systems Engineering DepartmentAdana Science and Technology UniversityAdanaTurkey
  5. 5.Mechanical Engineering DepartmentCukurova UniversityAdanaTurkey

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