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

M. Ozkan, D. Küçük, S. Buhan, T. Demirci, Pinar Karagoz
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引用次数: 2

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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基于智能数据分析的大规模可再生能源监测与预测
智能数据分析技术,如数据挖掘或统计/机器学习算法被应用于不同的领域,包括能源信息学。这些技术已被成功地用于解决能源领域内的不同问题,特别是预测问题,如可再生能源和能源消耗预测。本章详细阐述了使用智能数据分析技术促进可再生能源监测和预测。首先,回顾了应用于可再生能源领域的系统和预测方法的文献。其次,介绍了一种基于智能数据分析的通用、大规模可再生能源监测预测系统。最后,给出了该系统在风能领域的实际应用,并给出了其性能分析结果。考虑到所提供的概念和应用系统描述,本章是可再生能源信息学的重要参考,主要基于智能计算技术。
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