Wind energy is considered one of the fastest growing renewable ('green') energy resources. Precise wind power forecasting is imperative to ensure reliable power system planning and wind farm operation. However, traditional methods cannot yield satisfactory forecasts because of the chaotic properties and high volatility of wind speed time series. To address this issue, the use of artificial neural networks has attracted increasing attention owing to their significantly enhanced prediction accuracy. Based on these considerations, a novel neural model with a dynamic dendrite structure, known as the dendritic neuron model (DNM), can be adopted for wind speed time series prediction. The DNM is a plausible biological neural model that was originally designed for classification problems; accordingly, this study proposes the use of a regressive version of the DNM, named dendritic neural regression (DNR), in which the dendrite strength of each branch is considered. To enhance the prediction performance, the recently proposed states of matter search (SMS) optimization algorithm is used to optimize the neural architecture for DNR. By virtue of the powerful search ability of the SMS algorithm, DNR can efficiently capture the nonlinear correlations among distinct features and dendritic branches. Extensive experimental results and statistical tests demonstrate that compared with other state-of-the-art prediction techniques, DNR can achieve highly competitive results in wind speed forecasting.

中文翻译：

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通过树突神经回归预测风速时间序列


风能被认为是增长最快的可再生（“绿色”）能源之一。精确的风电功率预测对于确保可靠的电力系统规划和风电场运行至关重要。然而，由于风速时间序列的混沌特性和高波动性，传统方法无法产生令人满意的预测。为了解决这个问题，人工神经网络的使用因其显着提高的预测精度而引起了越来越多的关注。基于这些考虑，可以采用一种具有动态树突结构的新型神经模型，称为树突神经元模型（DNM），用于风速时间序列预测。 DNM 是一种合理的生物神经模型，最初是为分类问题而设计的；因此，本研究提出使用 DNM 的回归版本，称为树突神经回归（DNR），其中考虑每个分支的树突强度。为了提高预测性能，最近提出的物质状态搜索（SMS）优化算法被用来优化 DNR 的神经架构。凭借SMS算法强大的搜索能力，DNR可以有效捕获不同特征和树突分支之间的非线性相关性。大量的实验结果和统计测试表明，与其他最先进的预测技术相比，DNR 在风速预测方面可以取得极具竞争力的结果。