Hybrid Short-term Load Forecasting Method Based on Empirical Wavelet Transform and Bidirectional Long Short-term Memory Neural Networks  

作　　者：Xiaoyu Zhang Stefanie Kuenzel Nicolo Colombo Chris Watkins 

机构地区：[1]School of Artificial Intelligence,Anhui University,Hefei 230601,China [2]Department of Electronic Engineering,Royal Holloway,University of London,Egham Hill,Egham TW20 OEX,UK [3]Department of Computer Science,Royal Holloway,University of London,Egham Hill,Egham TW200EX,UK

出　　处：《Journal of Modern Power Systems and Clean Energy》2022年第5期1216-1228,共13页现代电力系统与清洁能源学报（英文）

基　　金：supported by the Leverhulme Trust,UK。

摘　　要：Accurate short-term load forecasting is essential to modern power systems and smart grids. The utility can better implement demand-side management and operate power system stably with a reliable load forecasting system. The load demand contains a variety of different load components, and different loads operate with different frequencies. The conventional load forecasting methods, e.g., linear regression(LR), auto-regressive integrated moving average(ARIMA), deep neural network, ignore the frequency domain and can only use time-domain load demand as inputs. To make full use of both time-domain and frequency-domain features of the load demand, a load forecasting method based on hybrid empirical wavelet transform(EWT) and deep neural network is proposed in this paper. The proposed method first filters noises via wavelet-based denoising technique, and then decomposes the original load demand into several sub-layers to show the frequency features while the time-domain information is preserved as well. Then, a bidirectional long short-term memory(LSTM) method is trained for each sub-layer independently. In order to better tune the hyperparameters, a Bayesian hyperparameter optimization(BHO) algorithm is adopted in this paper. Three case studies are designed to evaluate the performance of the proposed method.From the results, it is found that the proposed method improves the prediction accuracy compared with other load forecasting method.

关 键 词：Load forecasting empirical wavelet transform(EWT) recurrent neural network data denoising Bayesian hyperparameter optimization(BHO) 

分 类 号：TM715[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]