A multi-energy load prediction model based on deep multi-task learning and ensemble approach for regional integrated energy systems. (March 2021) - Xuan, Wang; Shouxiang, Wang; Qianyu, Zhao; Shaomin, Wang; Liwei, Fu
Highlights: CGRU can extract more efficient features and model time series dynamically. Different network structures are used to meet prediction needs of different loads. HUMTL is used to deeper dig coupling relations among various energy systems. GBRT realizes the sharing of prediction results learning in different degrees. Proposed multi-energy prediction model has advantages in accuracy and applicability. Abstract: Regional integrated energy system (RIES) plays an important role in the energy economy because of its advantages such as low environmental pollution and high efficiency cascade energy utilization. In order to ensure the operational efficiency and reliability of RIES, the accurate prediction of energy demand has become a crucial task. To this end, this paper proposes a novel multi-energy load prediction model based on deep multi-task learning and ensemble approach for RIES. Its novelty lies in the following four aspects: (1) considering the high-dimensional temporal and spatial features, a hybrid network based on convolutional neural network (CNN) and gated recurrent unit (GRU) is utilized to extract high-dimensional abstract features and model nonlinear time series dynamically; (2) to meet the prediction requirements of various loads, three GRU networks with different structures are designed, which can adapt to different types of loads with various fluctuations; (3) considering the coupling relations, an enhanced multi-task learning with homoscedastic uncertainty (HUMTL) is proposed, which can better make the prediction tasks of various loads achieve the optimum simultaneously; (4) to realize the sharing of learning results of different structure networks, ensemble approach based on gradient boosting regressor tree (GBRT) is adopted, which can make a weighted summary by the prediction results of various energy features learning in different degrees. Numerical example shows that the proposed model can dig the coupling relations among various energy systems deeper, explore the temporal and spatial correlation of multi-energy loads further, and it has higher prediction accuracy and better prediction applicability than other current advanced models.