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Non-Intrusive Load Monitoring describes the process of analyzing the aggregate household energy consumption to infer the individual energy consumption patterns of different appliances. Although NILM research has led to substantial progress as regards the performance of deep learning models, these models require exhaustive resources for the training phase and, due to their computational demand, are not well suited for deployment on edge devices with limited resources. NILM applications on low-resource devices enhance user adoption, opening up new energy market prospects. Although there has been some work toward edge-computed NILM, the proposed compression frameworks provide a solution only for the deployment phase since they are applied to the already trained models. This study presents OPT-NILM, a novel pruning strategy to discover sub-optimal NILM neural networks before full training, which reduces computing costs for both testing and training phase, and improves disaggregation performance compared to conventional after-training pruning. OPT-NILM proposes a metric to find the appropriate pruning threshold by evenly valuing model performance and computing cost, unlike other approaches that apply compression arbitrarily. Experimental results on the UK-Dale dataset show that the OPT-NILM approach may reduce model trainable parameters by up to 95&#x0025; with minimal performance loss.

OPT-NILM: An Iterative Prior-to-Full-Training Pruning Approach for Cost-Effective User Side Energy Disaggregation