Short-Term Load Forecasting Method based on Empirical Wavelet
Decomposition and BLSTM Neural Networks
Abstract
Accurate short-term load forecasting is essential to the modern power
system and smart grids; the utility can better implement demand-side
management and operate the power system stable with a reliable
forecasting system. The load demand contains a variety of different load
components, and different loads operate with different frequencies.
Conventional load forecasting models (linear regression (LR),
Auto-Regressive Integrated Moving Average (ARIMA), deep neural network,
etc.) ignore 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 hybrid component decomposition and deep
neural network load forecasting model is proposed in this paper. The
proposed model first filters noises via wavelet-based denoising
technique, then decomposes the original load demand into several
sublayers to show the frequency features while the time domain
information is preserved as well. Then bidirectional LSTM model is
trained for each sub-layer independently. To better tunning the
hyperparameters, a Bayesian hyperparameter optimization algorithm is
adopted in this paper. Three case studies are designed to evaluate the
performance of the proposed model. From the results, it is found that
the proposed model improves RMSE by 66.59% and 84.06%, comparing to
other load forecasting models.