Integrating Graph Signal Processing and Multitask Temporal Convolutional Networks for Household Nonintrusive Load Monitoring
Yongxin Su, Haotian Peng, Mao Tan, Jie Chen
Abstract
Highly accurate non-intrusive load monitoring (NILM) models are essential for energy management, optimization decisions, and system monitoring. However, the sparsity of loads features and spatio-temporal relationships hidden in loads have not been fully tackled, hindering the accuracy of NILM models. To solve the above problems, a load disaggregation (LD) framework that cascades graph signal processing (GSP) with multitask temporal convolution network (TCN) is proposed. In this framework, the GSP is designed as a direct load feature extractor to tackle the sparsity of load features. The multitask TCN can leverage GSP outputs and total power to extract spatio-temporal relationships among loads, and then generates precise LD results for each load simultaneously. Afterwards, the implementation of the GSP based direct load feature extractor is designed, including the construction of graph representation of load features, pattern matching, and direct load features correction. Then the implementation scheme of multitask TCN is proposed, consisting of load features fusion strategy, the spatio-temporal relationships extractor design, and the loss function setting and training strategy. Experiment shows that our model can concurrently output LD results for 8 appliances. Meanwhile, compared to existing advanced methods, our model has an over 13% reduction on mean absolute error.