Contextual Pooling for Multiple-Instance Learning-based Non-Intrusive Load Monitoring

Understanding detailed energy consumption patterns is crucial for optimizing resource allocation in microgrids, which often incorporate renewable energy sources and stationary batteries. In this context, this paper addresses the Non-intrusive Load Monitoring problem of appliance-state classification, providing insights into appliance-specific usage by using only the aggregate active power consumed in a building. Deep Neural Networks represent the state of the art in this field, but they require a significant amount of data for training. Consequently, in recent years, the research community has explored weakly supervised approaches. Although weakly supervised methods have proven effective, they rely on the use of pooling functions to calculate bag-level predictions from instance-level ones. These functions should be intrinsically related to the dynamics of the input signal, and while several alternatives have been proposed in the literature, none have specifically addressed the characteristics of appliance power profiles. This paper fills this gap by introducing a new pooling function, Contextual Pooling, that operates on multiple adjacent instance-level predictions to better capture the dynamics of appliance activations. This approach has been evaluated against six different methods on the UK-DALE and REFIT datasets. The results indicate that, on average, Contextual Pooling improves performance by at least 0.9 percentage points on UK-DALE and by 0.7 percentage points on REFIT compared to benchmark methods. McNemar’s test confirms the statistical significance of these improvements.