Deep belief network based deterministic and probabilistic wind speed forecasting approach
Deep belief network based deterministic and probabilistic wind speed forecasting approach
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基于深度信念网络的确定性和概率性风速预测方法
DOI：
10.1016/j.apenergy.2016.08.108
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发表时间：
2016-11-15
期刊：
影响因子：
11.2
通讯作者：
Liu, Y. T.
中科院分区：
文献类型：
--
作者：
Wang, H. Z.;Wang, G. B.;Liu, Y. T.
随着风电进入现代电网的快速增长，风速预测（WSF）在电力和能源系统的规划和运行中发挥着越来越重要的作用。然而，风速时间序列始终表现出非线性和非平稳特征，导致其准确预测非常困难。认识到这一挑战，针对确定性和概率性 WSF 提出了一种基于深度学习的新颖方法。该方法是小波变换（WT）、深度置信网络（DBN）和脊椎分位数回归（QR）的混合体。小波变换用于将原始风速数据分解为具有更好性能的不同频率序列。基于DBN的分层预训练完全提取了每个频率的非线性特征和不变结构。然后，通过QR方法对风速的不确定性进行统计综合。介绍了使用中国和澳大利亚真实风电场数据的案例研究。比较结果表明，可以更好地学习风速序列中的高级非线性和非平稳特征，从而获得有竞争力的性能。因此，我们相信所提出的方法在电力和能源系统中具有很高的实际应用潜力。 (C) 2016 Elsevier Ltd. 保留所有权利。
With the rapid growth of wind power penetration into modern power grids, wind speed forecasting (WSF) plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the wind speed time series always exhibits nonlinear and non-stationary features, making it very difficult to be predicted accurately. Recognizing this challenge, a novel deep learning based approach is proposed for deterministic and probabilistic WSF. The approach is a hybrid of wavelet transform (WT), deep belief network (DBN) and spine quantile regression (QR). WT is employed to decompose raw wind speed data into different frequency series with better behaviors. The nonlinear features and invariant structures of each frequency are completely extracted by layer-wise pre-training based DBN. Then, the uncertainties in wind speed are statistically synthesized via the QR method. Case studies using real wind farm data from China and Australia have been presented. The comparative results demonstrate that the high-level nonlinear and non-stationary feature in the wind speed series can be learned better, and competitive performance can thus be obtained. It is therefore convinced that the proposed method has a high potential for practical applications in electric power and energy systems. (C) 2016 Elsevier Ltd. All rights reserved.