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Journals / International Journal of Intelligent Systems and Applications in Engineering / 2016 / Cilt: 4 Sayı: Special Issue-1

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

Ümit Çiğdem TURHAL
Türker DEMİRCİ
Pages
175–179
Publication Date
2016-12-26
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:The performance of Learning Systems has been raised in latest years such as it sometimes exceeds the performance of humans.
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Conclusion & Impact: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.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Energy efficiency · Source installation · Estimation

Abstract

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.

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Cite This Article

TURHAL, Ü. Ç., DEMİRCİ, T. (2016). A Survey on Learning System Applications in Energy System Modeling and Prediction. International Journal of Intelligent Systems and Applications in Engineering, Cilt: 4 Sayı: Special Issue-1, 175–179. https://ajindex.com/articles/995967

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