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Large-Scale Renewable Energy Monitoring and Forecast Based on Intelligent Data Analysis

Large-Scale Renewable Energy Monitoring and Forecast Based on Intelligent Data Analysis
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Author(s): Mehmet Baris Ozkan (Energy Institute, TÜBİTAK Marmara Research Center, Turkey), Dilek Küçük (Energy Institute, TÜBİTAK Marmara Research Center, Turkey), Serkan Buhan (Energy Institute, TÜBİTAK Marmara Research Center, Turkey), Turan Demirci (Energy Institute, TÜBİTAK Marmara Research Center, Turkey) and Pinar Karagoz (Computer Engineering Deptartment, Middle East Technical University, Turkey)
Copyright: 2020
Pages: 25
Source title: Handbook of Research on Smart Computing for Renewable Energy and Agro-Engineering
Source Author(s)/Editor(s): Valeriy Kharchenko (Federal Scientific Agroengineering Center VIM, Russia) and Pandian Vasant (Universiti Teknologi Petronas, Malaysia)
DOI: 10.4018/978-1-7998-1216-6.ch003

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Abstract

Intelligent data analysis techniques such as data mining or statistical/machine learning algorithms are applied to diverse domains, including energy informatics. These techniques have been successfully employed in order to solve different problems within the energy domain, particularly forecasting problems such as renewable energy and energy consumption forecasts. This chapter elaborates the use of intelligent data analysis techniques for the facilitation of renewable energy monitoring and forecast. First, a review of the literature is presented on systems and forecasting approaches applied to the renewable energy domain. Next, a generic and large-scale renewable energy monitoring and forecast system based on intelligent data analysis is described. Finally, a genuine implementation of this system for wind energy is presented as a case study, together with its performance analysis results. This chapter stands as a significant reference for renewable energy informatics, considering the provided conceptual and applied system descriptions, heavily based on smart computing techniques.

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