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Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

机译：改进克隆选择算法训练的小波神经网络风电功率预测

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With the integration of wind farms into electric power grids, an accurate wind power prediction is becoming increasingly important for the operation of these power plants. In this paper, a new forecasting engine for wind power prediction is proposed. The proposed engine has the structure of Wavelet Neural Network (WNN) with the activation functions of the hidden neurons constructed based on multi-dimensional Morlet wavelets. This forecast engine is trained by a new improved Clonal selection algorithm, which optimizes the free parameters of the WNN for wind power prediction. Furthermore, Maximum Correntropy Criterion (MCC) has been utilized instead of Mean Squared Error as the error measure in training phase of the forecasting model. The proposed wind power forecaster is tested with real-world hourly data of system level wind power generation in Alberta, Canada. In order to demonstrate the efficiency of the proposed method, it is compared with several other wind power forecast techniques. The obtained results confirm the validity of the developed approach.
机译：随着将风电场整合到电网中，准确的风能预测对于这些电厂的运行变得越来越重要。本文提出了一种用于风电功率预测的新型预测引擎。所提出的引擎具有小波神经网络（WNN）的结构，具有基于多维Morlet小波构造的隐藏神经元的激活功能。该预测引擎由新的改进的克隆选择算法训练，该算法优化了WNN的自由参数以进行风能预测。此外，在预测模型的训练阶段，已使用最大熵准则（MCC）代替均方误差作为误差度量。拟议的风力发电预报器已经在加拿大艾伯塔省的系统级风力发电实时小时数据中进行了测试。为了证明该方法的有效性，将其与其他几种风能预测技术进行了比较。获得的结果证实了所开发方法的有效性。

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