Enhancing real-time and day-ahead load forecasting accuracy with deep learning and weighed ensemble approach

Economic dispatching of power system includes real-time dispatching and day-ahead dispatching. In this process, accurate real-time and day-ahead load forecasting is crucial. However, integrating real-time forecasting and day-ahead forecasting into one system, and ensuring that both have good performance, is a challenging problem. To solve the above problem, we propose a load forecasting system based on deep learning and weighted ensemble. The system is composed of the high precision prediction module and the intelligent weighted ensemble module. In the high precision prediction module, we use variational mode decomposition (VMD) to decompose the data into multiple components of different frequencies, and build a selection pool that includes statistical models and deep learning to select the best prediction model for each component through customed metrics. In the intelligent weighted ensemble module, we improve the Grey Wolf optimization algorithm with tent chaos mapping and flight strategy. The improved Grey Wolf optimization algorithm (ILGWO) is used to determine the weight of each component, then the weight is multiplied by the component prediction result, and the final prediction result is obtained by adding. To verify the superiority of the proposed forecasting system, we conducted experiments using four sets of load data from New South Wales, Australia. Through six groups of experiments and three groups of discussion, the accuracy, stability and applicability of the load forecasting system are verified. Compared with the traditional method, the prediction accuracy (MAPE) of the proposed load forecasting system is improved by about 55%. In addition, we further validated the generality of the system with four sets of load data from Queensland, Australia. The results show that the proposed load forecasting system is significantly superior to other models and provides more reliable load forecasting for power system management and scheduling.