基于门控循环神经网络的边缘服务中心风光荷组合预测方法
摘要：边缘计算因数据处理快、低成本和高实时等优点近年来在能源行业中受到广泛关注，而在边缘服务器上开展预测有助于对能源精细化管控。因此，针对边缘服务资源的有限性，基于差分自回归整合移动平均（autoregressive integrated moving average, ARIMA）模型和门控循环单元（gated recurrent unit, GRU）神经网络，提出考虑线性和非线性特征的风、光、荷组合预测方法。ARIMA用于提取源、荷的线性特征，将其与真实值进行拟合，得到包含非线性特征的残差。其次，将残差作为GRU的训练数据集建立预测模型，再引入剪枝和量化方法优化及压缩GRU模型，减小预测模型规模，以适应边缘服务器部署。大量仿真结果表明，所构建的GRU压缩模型规模小、预测精度高，适合边缘服务器的部署应用。
Abstract:Edge computing is extensively concerned by the energy industry because of the advantages of fast data processing, low cost and high real-time, and the prediction on the edge server is helpful for the refined management and control of energy. For this reason, in allusion to the limitations of edge service resources, based on difference autoregressive integrated moving average (ARIMA) model and gated recurrent unit (GRU）neural network a combined prediction method of wind, light and load was proposed. Firstly, the ARIMA was used to extract the linear characteristics of source and load, and through fitting linear characteristics with the true value the residual with nonlinear features was obtained. Secondly, taking the residual as the training dataset of GRU a prediction model was established, and then leading in the pruning and quantification method the GRU model was optimized and compressed to reduce the size of the prediction model to suit the deploy of edge servers. Results of lots of simulation examples show that the constructed GRU compression model possesses the features of small scale and high prediction accuracy, so it is suitable to the deployment and application of edge servers.
标题：基于门控循环神经网络的边缘服务中心风光荷组合预测方法
英文标题：Combined Prediction Method for Wind-photovoltaic-load in Edge Service Center Based on ARIMA-GRU
