Robust Wavelet Transform Neural-Network-Based Short-Term Load Forecasting for Power Distribution Networks

Abstract

A precise short-term load-forecasting model is vital for energy companies to create accurate supply plans to reduce carbon dioxide production, causing our lives to be more environmentally friendly. A variety of high-voltage-level load-forecasting approaches, such as linear regression (LR), autoregressive integrated moving average (ARIMA), and artificial neural network (ANN) models, have been proposed in recent decades. However, unlike load forecasting in high-voltage transmission systems, load forecasting at the distribution network level is more challenging since distribution networks are more variable and nonstationary. Moreover, existing load-forecasting models only consider the features of the time domain, while the demand load is highly correlated to the frequency-domain information. This paper introduces a robust wavelet transform neural network load-forecasting model. The proposed model utilizes both time- and frequency-domain information to improve the model's prediction accuracy. Firstly, three wavelet transform methods, variational mode decomposition (VMD), empirical mode decomposition (EMD), and empirical wavelet transformation (EWT), were introduced to transform the time-domain demand load data into frequency-domain data. Then, neural network models were trained to predict all components simultaneously. Finally, all the predicted data were aggregated to form the predicted demand load. Three cases were simulated in the case study stage to evaluate the prediction accuracy under different layer numbers, weather information, and neural network types. The simulation results showed that the proposed robust time–frequency load-forecasting model performed better than the traditional time-domain forecasting models based on the comparison of the performance metrics, including the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE).

P-ConvLSTM: An Effective Parallel ConvLSTM-based model for Short-term Electricity Load Forecasting

“…They trained a multi-headed temporal convolutional network using historical load, calendar variables, and weather variables to forecast 1-step, 5-steps, and 10-steps future load values. In [27], the author proposed a hybrid STLF model for volatile and non-stationary distribution networks using a robust wavelet transformation and neural network. Their simulation show better results when compared with traditional forecasting models.…”

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

“…Although the first-generation methods were successful in the past, researchers continue to utilize them for feature extraction purposes [17,18]. In recent years, ANN-based models have been widely adopted, primarily due to their proficiency in processing nonlinear data.…”

“…The dataset comprises two years of hourly demand data for a city region, considering various features such as temperature, humidity, and holidays. Wang et al in [17] suggested using variational mode decomposition (VMD), empirical mode decomposition (EMD), and empirical wavelet transform (EWT) to convert time-domain demand data into the frequency domain. The processed data are then passed to a Bi-LSTM layer before signal reconstruction at the output.…”

BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems

“…Their models, VMD-CNN-long short-term memory (LSTM) and VMD-CNN-gated recurrent unit (GRU), showcased versatility, adeptly managing seasonal and daily energy consumption variations. Wang et al [27], in their recent endeavors, proposed a wavelet transform neural network that uniquely integrates time and frequency-domain information for load forecasting. Their model leveraged three cutting-edge wavelet transform techniques, encompassing VMD, empirical mode decomposition (EMD), and empirical wavelet transform (EWT), presenting a comprehensive approach to forecasting.…”