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.

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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.