In the study of non-intrusive load monitoring, using a single feature for identification can lead to insignificant differentiation of similar loads; however, multi-feature fusion can pool the advantages of different features to improve identification accuracy. Based on this, this paper proposes a recognition method based on feature fusion and matrix heat maps, using V-I traces, phase and amplitude of odd harmonics, and fundamental amplitude. These are converted into matrix heat maps, which can retain both large and small eigenvalues of the same feature for different loads and can retain different features. The matrix heat map is recognized by using SE-ResNet18, which avoids the problem of the classical CNN depth being too deep, causing network degradation and being difficult to train, and achieves trauma-free monitoring of home loads. Finally, the model is validated using the PLAID and REDD datasets, and the average recognition accuracy is 96.24% and 96.4%, respectively, with significant recognition effects for loads with similar V-I trajectories and multi-state loads.

The optimal management of hydropower resources is highly dependent on accurate and reliable hydrological runoff forecasting. The development of a suitable runoff-forecasting model is a challenging task due to the complex and nonlinear nature of runoff. To meet the challenge, this study proposed a three-stage novel hybrid model namely IVG (ICEEMDAN-VMD-GRU), by coupling gated recurrent unit (GRU) with a two-stage signal decomposition methodology, combining improved complete ensemble empirical decomposition with additive noise (ICEEMDAN) and variational mode decomposition (VMD), to forecast the monthly runoff of SWAT river, Pakistan. ICEEMDAN decomposed the runoff time series into subcomponents, and VMD performed further decomposition of the high-frequency component obtained by ICEEMDAN decomposition. Afterward, the GRU network was employed to the decomposed subcomponents for forecasting purposes. The performance of the IVG model was compared with other hybrid models including, ICEEMDAN-VMD-SVM (support vector machine), ICEEMDANGRU, VMD-GRU, ICEEMDAN-SVM, VMD-SVM; and standalone models including GRU and SVM by utilizing statistical indices. Experimental results proved that the IVG model outperformed other models in terms of accuracy and error reduction, which indicates the feasibility of the IVG model to analyze the nonlinear features of runoff time series and for runoff forecasting with applicability for future planning and management of water resources.

Hydrological runoff prediction in a reliable and precise manner contributes significantly to the optimal management of hydropower resources. Considering the importance of runoff prediction, this study proposed a hybrid model, namely VBH (VMD-BP), coupling variational mode decomposition (VMD) technique, and backpropagation (BP) based artificial neural network (ANN), to predict the monthly runoff of Fentang reservoir, China. Two hybrid models, including ensemble empirical mode decomposition-BP (EEMD-BP) and empirical mode decomposition-BP (EMD-BP), and a standalone BP model, were also developed for comparative analysis. The VBH model performed better compared to the EEMD-BP model in reducing mean absolute error (MAE) by 40.263%, root mean square error (RMSE) by 33.634%, and mean absolute percentage error (MAPE) by 52.906%. The improved results for the VBH model compared to the EMD-BP model included 103.716, 82.266, and 158.303% reductions in MAE, RMSE, and MAPE, respectively. The error reductions by the VBH model compared to the BP model were 113.848% for MAE, 122.022% for RMSE, and 143.026% for MAPE. The results highlighted that the proposed model was superior to the hybrid and standalone counterparts for the hydrological runoff prediction. Water resources designers and planners for future planning and management of hydrological assets can exploit the proposed model.

Accurate forecasting of flexible loads can capture the potential of their application and improve the adjustable space of the distribution network. Flexible load data, such as air conditioning (AC) and electric vehicles (EV), are generally included in the total load data, making it difficult to forecast them directly. To this end, this paper proposes a multi-step flexible load prediction model based on the non-intrusive load decomposition technique and Informer algorithm. The CNN-BiLSTM model is first used to decompose the flexible load from the total load via feature extraction and feature mapping of the flexible load to the overall load. The Informer model is then used to predict the flexible load and the residual load separately in multiple steps, and the prediction results are summed to obtain the overall prediction results. In this paper, the model is validated using two datasets, where in dataset 1, the prediction coefficients of determination for flexible load air conditioning and electric vehicles are 0.9329 and 0.9892. The predicted value of the total load is obtained by adding the flexible load to the residual load. At a prediction step of 1, the total load prediction coefficient of determination is 0.9813, which improves the prediction coefficient of determination by 0.0069 compared to the direct prediction of the total load, and prediction decision coefficient improves by 0.067 at 20 predicted steps. When applied to data set 2, the prediction coefficient of determination for flexible load air conditioning is 0.9646.

The charge/discharge operation of the prosumer’s energy storage and the energy interaction between prosumers and MGs are chaotic from the overall point of the MG’s operation. It causes considerable resource waste and reduces the overall benefits of the MG with multi-prosumers. Therefore, a game theory-based optimal scheduling strategy for the MG with multi-prosumers combined into a PRCO is proposed in this paper. According to the prosumers’ complementary characteristics of ES utilization and energy production, prosumers can be integrated into the PRCO to obtain energy reciprocity by sharing ES with an ordered charge–discharge operation. Meanwhile, to improve the collaboration of prosumers and the overall efficiency of the MG, a game scheduling model is established with the MG as the leader and the PRCO as the follower. The ToU price incentive policy is implemented in the MG to maximize the operational benefits and reduce the difference between the valley and peak load. Meanwhile, the PRCO responds to the price policy and implements an ordered charge–discharge strategy of ES to optimize each member’s energy scheduling strategy and minimize the total costs. The PRCO revenues are distributed to prosumers based on the Shapley value method. The uniqueness and existence of Stackelberg equilibrium in the game model are proved. The simulations of a community MG show that the ordered charge–discharge operation of ES is achieved and the overall benefits of the system are improved.