A Survey on Learning System Applications in Energy System Modeling and Prediction

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 175 - 179, 26.12.2016

Ümit Çiğdem Turhal , Türker Demirci

Öz

Learning Systems (LS) such as
machine learning, statistical pattern recognition and neural networks are
computer programs that can learn from sample data and develop a prediction
model that makes prediction for new cases. The most important think related with
a prediction model is to achieve results as closer as to real situation while
making predictions. This is important because being closer to real results help
to reduce the costs of feasibility studies in system installation. The
performance of Learning Systems has been raised in latest years such as it
sometimes exceeds the performance of humans. That’s why the applications of
Learning Systems have been increased in many areas. This paper reviews the
present applications of Learning Systems in energy system modeling and
prediction especially in renewable energy systems such as wind and solar. The
aim of this paper is to create a vision for researchers by gathering the
present applications and outline their merits and limits and the prediction of
their future performance on specific applications. 

Anahtar Kelimeler

Energy efficiency, Source installation, Estimation

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