Enhancing Load Forecasting Accuracy in Smart Grids: A Novel Parallel Multichannel Network Approach Using 1D CNN and Bi-LSTM Models

Abstract

Load forecasting plays a pivotal role in the efficient energy management of smart grid. However, the complex, intermittent, and nonlinear smart grids and the complexity of large dataset handling pose difficulty in accurately forecasting loads. The important issue is considering the cyclic features, which have not yet been adequately addressed through the trigonometric transformations. Furthermore, using long short-term memory (LSTM) or 1D convolution neural network (1D CNN) and existing hybrid models involve stacked CNN-LSTM architectures, employing 1D convolutions as a preprocessing step to downsample sequences and extract high- and low-level spatial features. However, these models often overlook temporal features, emphasizing higher-level features processed by the subsequent recurrent neural network layer. Therefore, this study considers a novel approach to independently process features for spatial and temporal characteristics using a parallel multichannel network comprising 1D CNN and bidirectional-LSTM (Bi-LSTM) models. The proposed model evaluated the National Transmission and Dispatch Company (NTDC) load dataset, with additional assessment on two datasets, American Electric Power and Commonwealth Edison, to ensure its generalizability. Performance evaluation on the NTDC dataset yields results of 3.4% mean absolute percentage error (MAPE), 513.95 mean absolute error (MAE), and 623.78 root mean square error (RMSE) for day-ahead forecasting, and 0.56% MAPE, 94.84 MAE, and 115.67 RMSE for hour-ahead load forecast. The experimental results demonstrate that the proposed model outperforms stacked CNN-LSTM models, particularly in forecasting hour- and day-ahead loads. Moreover, a comparative analysis with previous studies reveals superior performance in reducing the error gap between predicted and actual values.