A Novel Two-Stage Framework for Mid-Term Electric Load Forecasting

Electric utilities and planners rely heavily on accurate mid-term load projections to effectively schedule maintenance, coordinate load dispatch, and manage fuel reserves. Current approaches that attempt to forecast electric load for multiple horizons in a single stage, however, often result in inaccurate forecasts due to the lack of information for predicting the data at the farthest horizon and propagation of large errors. To address this issue, this article presents a two-step mechanism for forecasting electric load that utilizes the benefits of both backpropagation neural networks (BPNNs) and radial basis function neural networks (RBFNNs). The first stage utilizes a pool of BPNNs to make partial predictions. BPNNs are used as they are well-known for their capability of fast and accurate predictions. In the second stage, RBFNNs are used to make complete forecasts. RBFNNs are known for their ability to model nonlinear relationships in the data which is crucial in this stage of the forecast process. Additionally, this article also proposes a unique method for generating additional training samples by incorporating stacking noise, which adds more variability to the data, increasing the generalization capability of the models, resulting in better forecasting performance. The performance of the proposed framework has been investigated with simulation studies and has been validated on actual load data of the Madhya Pradesh Power Transmission System, India. The performance comparison with the existing methods in terms of both accuracy and reliability shows that the proposed forecasting framework provides more accurate results.