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Enhanced support vector regression based forecast engine to predict solar power output

机译：基于增强支持向量回归的预测引擎来预测太阳能输出

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摘要

The critical role of photovoltaic (PV) energy as renewable sources in network can make some problems in power grids operation. Due to high volatility of PV signal, the prediction and its evaluation in planning and operation is very difficult. For this purpose, an accurate prediction approach is developed in this paper to tackle the mentioned problem. The proposed approach is based on enhanced empirical model decomposition (EEMD), a new feature selection method and hybrid forecast engine. The proposed feature selection is formulated by different criteria to select the best candidate inputs of forecast engine. And finally the hybrid forecast engine composed of improved support vector regression (ISVR) plus optimization algorithm to fine tune the related free parameters. Effectiveness of proposed method is applied over real-world engineering test cases through comparison with various prediction models. (C) 2018 Elsevier Ltd. All rights reserved.
机译：光伏（PV）能源作为网络中的可再生资源的关键作用可能会在电网运行中产生一些问题。由于光伏信号的高波动性，因此在计划和运营中进行预测和评估非常困难。为此，本文开发了一种精确的预测方法来解决上述问题。所提出的方法基于增强型经验模型分解（EEMD），新的特征选择方法和混合预测引擎。拟议的特征选择由不同的标准制定，以选择预测引擎的最佳候选输入。最后，混合预测引擎由改进的支持向量回归（ISVR）加上优化算法组成，以微调相关的自由参数。通过与各种预测模型进行比较，将所提方法的有效性应用于实际工程测试案例。 （C）2018 Elsevier Ltd.保留所有权利。

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