A novel data-driven multi-energy load forecasting model

With the increasing concern on energy crisis, the coordination of multiple energy sources and low-carbon economic operation of integrated energy system (IES) have drawn more and more attention in recent years. In IES, accurate and effective multi-energy load forecasting becomes a research hotspot, especially using the high-performance data mining and machine learning algorithms. However, due to the huge difference in energy utilization between IES and traditional energy systems, the load forecasting of IES is more difficult and complex. In fact, in IES, load forecasting is not only related to external factors such as meteorological parameters and different seasons, but the correlation between energy consumption of different types of loads also plays an important role. In order to deal with the strong coupling and high uncertainty issues in IES, a novel data-driven multi-energy load forecasting model is proposed in this paper. Firstly, a feature extraction method based on Uniform Manifold Approximation and Projection (UMAP) for multi-energy load of the IES is developed, which reduces the dimension of the complex nonlinear input data. Then, considering multi-energy coupling correlation, a combined TCN-NBeats model is proposed for the joint prediction of multi-energy loads, aiming to improve the prediction accuracy through ensemble learning. Finally, the numerical case analysis using the multi-energy consumption data of an actual campus verifies the effectiveness and accuracy of the proposed data-driven multi-energy load forecasting model.