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Abstract Non-intrusive load identification (NILI) is a core function of non-intrusive load monitoring (NILM) systems, aiming to recognize individual appliance operating states from aggregated electrical measurements collected at a single monitoring point. In practical residential and industrial environments, NILI performance is often constrained by complex operating conditions, including simultaneous appliance operation, similarity in steady-state power levels, strong temporal variability, measurement noise, and limited labeled training data. Conventional time-series models, such as long short-term memory (LSTM) and gated recurrent unit (GRU) networks, rely on single-form memory update mechanisms. Consequently, their ability to jointly represent short-duration transient behaviors and long-term operational patterns is limited, particularly under non-stationary and noisy load conditions. These challenges motivate the development of more expressive and flexible sequence modeling frameworks for robust load identification.
This paper presents an end-to-end load identification framework integrating multi-scale convolutional feature extraction, attention-based feature modulation, and extended long short-term memory (xLSTM) temporal modeling. The framework is designed to enhance feature discriminability and temporal dependency modeling while maintaining moderate computational complexity suitable for practical NILM applications. By jointly modeling local transient responses, global contextual information, and long-range temporal dependencies within a unified architecture, the proposed method improves robustness and generalization in diverse load scenarios.
In the feature extraction stage, a dual-branch multi-scale convolutional structure analyzes power signals at different temporal resolutions. One branch employs convolution kernels with larger receptive fields combined with down-sampling operations to capture coarse-grained global trends and long-term power variations. The other branch stacks multiple small-kernel convolutional layers to extract fine-grained local details associated with transient events and short-duration fluctuations. To enhance nonlinear representation capability, a self-gated activation function with learnable parameters adaptively modulates channel-wise feature responses. In addition, a coordinate attention mechanism encodes channel dependencies together with temporal positional information, enabling selective emphasis on discriminative load segments while suppressing irrelevant fluctuations. The outputs of the two branches are spatially aligned and fused through element-wise operations, forming a compact representation that preserves complementary multi-scale characteristics.
For temporal modeling, xLSTM units composed of scalar long short-term memory (sLSTM) and matrix long short-term memory (mLSTM) modules are employed. Unlike conventional recurrent networks that update memory states using a single scalar representation, xLSTM provides more flexible and structured state representations through coordinated scalar and matrix memory updates. This design enables simultaneous modeling of short-term transient patterns and long-term operational cycles, improving robustness under noisy, overlapping, and non-stationary load conditions. By stacking xLSTM layers after the convolution-attention encoder, hierarchical spatio-temporal feature representations and long-range dependencies are effectively integrated, facilitating accurate load identification across varying operating durations.
The proposed method is evaluated on four representative public datasets: UK-DALE (UK Domestic Appliance-Level Electricity), LIFTED (Labelled High Frequency Dataset for Electricity Disaggregation), PLAID (Plug-Load Appliance Identification Dataset), and WHITED (Worldwide Household and Industry Transient Energy Data Set). These datasets cover diverse acquisition conditions, including long-term residential monitoring, controlled laboratory environments, transient-rich signals with strong noise, and high-fidelity waveform measurements. A unified preprocessing, training, and evaluation strategy is applied across all datasets to ensure fair comparison. Performance is evaluated based on five metrics: weighted precision, weighted recall, weighted F1-score, overall accuracy, and Matthews correlation coefficient (MCC). This comprehensive set of metrics is chosen to holistically reflect classification effectiveness under challenging conditions, including class imbalance and diverse misclassification patterns.
Experimental results demonstrate that the proposed framework achieves competitive and stable performance across different datasets and evaluation metrics. Compared with conventional recurrent models, convolutional neural networks, and representative hybrid deep learning approaches, the proposed method exhibits improved robustness and more balanced metric performance, particularly in scenarios involving long-duration appliance operation, similar power levels, and strong noise interference. Ablation studies further confirm that joint modeling of transient and steady-state features, the selection of discriminative segments, and the representation of long-term dependencies are enhanced, improved, and strengthened by multi-scale convolution, attention mechanisms, and xLSTM, respectively. The synergistic integration of these components results in the observed improvements in generalization capability and performance stability.
In summary, the proposed multi-scale convolution-attention-xLSTM framework offers a robust and generalizable solution for non-intrusive load identification under complex operating conditions. By jointly addressing feature extraction, temporal modeling, and noise robustness within a unified architecture, the method significantly improves load identification accuracy across diverse datasets and application scenarios, thereby advancing the development of practical NILM systems and energy monitoring applications.
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Received: 30 September 2025
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2026, Vol. 41 
