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A novel ensemble method for residential electricity demand forecasting based on a novel sample simulation strategy
Energy ( IF 9 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.energy.2020.118265
Guoqiang Zhang , Jifeng Guo

Abstract This paper presents a novel ensemble method of forecasting the residential electricity demand. Firstly, the time-series of the original input variables is filtered by unscented kalman filter (UKF), and then the incremental percentages of current and previous sample points are taken as new input features of the proposed method. Secondly, an improved coupled generative adversarial stacked auto-encoder (ICoGASA) consisting of three generative adversarial networks (GAN) is developed to generate more similar errors in weather forecast and lifestyles of different residents, with less noise. All of the three GANs are composed of two deep belief networks (DBNs), which serve as generator and discriminator, respectively. The three generators of GANs are used to simulate the samples with positive error, negative error and mixed error, respectively. Then the output of the three discriminators is integrated by memristor array (MA), and the integrated output of each ICoGASA are integrated by self-organizing map (SOM). Thirdly, the input weights of SOM are optimized by MA and a new weight updated strategy (WUS). Compared with other state-of-the-art ensemble methods, the scopes of the root mean square error (RMSE) are reduced by [8.295, 16.221] %, [15.507, 28.066] %, [20.494, 36.969] %, respectively.

中文翻译:

基于新样本模拟策略的住宅用电需求预测新集成方法

摘要 本文提出了一种新的综合预测居民用电需求的方法。首先,原始输入变量的时间序列通过无迹卡尔曼滤波器(UKF)过滤,然后将当前和先前样本点的增量百分比作为所提出方法的新输入特征。其次,开发了由三个生成对抗网络(GAN)组成的改进的耦合生成对抗堆叠自动编码器(ICoGASA),以在不同居民的天气预报和生活方式中产生更多相似的错误,并且噪音更少。所有三个 GAN 都由两个深度信念网络 (DBN) 组成,分别作为生成器和判别器。GAN 的三个生成器分别用于模拟具有正误差、负误差和混合误差的样本。然后通过忆阻器阵列(MA)对三个鉴别器的输出进行积分,通过自组织映射(SOM)对每个ICoGASA的积分输出进行积分。第三,SOM 的输入权重通过 MA 和新的权重更新策略 (WUS) 进行优化。与其他最先进的集成方法相比,均方根误差 (RMSE) 的范围分别减小了 [8.295, 16.221] %、[15.507, 28.066] %、[20.494, 36.969] %。
更新日期:2020-09-01
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