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

• Published in: Journal of modern power systems and clean energy 2022-01, Vol.10 (5), p.1216-1228
• Main Authors: Xiaoyu Zhang, Stefanie Kuenzel, Nicolo Colombo, Chris Watkins
• Format: Article
• Language: English
• Subjects: Bayesian hyperparameter optimization (BHO); data denoising; empirical wavelet transform (EWT); Load forecasting; recurrent neural network
• ISSN: 2196-5420
• DOI: 10.35833/MPCE.2021.000276

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 hyper-parameters, a Bayesian hyperparameter optimization (BHO) algorithm is adopted in this paper. Three case studies are de-signed 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.