A multi-expert deep learning framework for accurate and scalable appliance-level energy disaggregation

Accurate identification of individual appliance activation and power consumption is essential for effective energy consumption analysis in residential buildings. Non-Intrusive Load Monitoring (NILM) allows the disaggregation of appliance consumption using the aggregate household power without the need for sensing. Despite the usefulness of NILM, the existing models struggle with the tasks of generalization, overlapping appliance signatures, and the presence of varying operating conditions. In this paper, a novel MoE-BiGRU-Transformer architecture is proposed. This architecture includes the combination of a mixture-of-expert routing method, bi-directional temporal information modeling, and self-attention under the paradigm of multi-task NILM framework, representing a unified integration of existing components for NILM. For this purpose, the MoE-BiGRU-Transformer model captures bidirectional temporal dependencies, global contextual features, and appliance-specific patterns through a self-attention process using the Transformer encoder component and learns appliance specialization through a gating function learning approach using the MoE component. A multi-task loss function is defined to train our model. Extensive experiments were conducted using two benchmark datasets: REDD and UK-DALE, while varying the number of epochs trained and the random seed. These results showed that the proposed model had average accuracy over 99%, high f1-score for most appliances, and low mean absolute error (MAE). Cross-dataset experiments showed high generalization, with accuracy and f1-score largely preserved when models were trained on one dataset and evaluated on the other. Ablation tests also showed that BiGRU and normalization components contributed to improved temporal resolution and stability of convergence.