Cluster Aggregated GAN (CAG): A Cluster-Based Hybrid Model for Appliance Pattern Generation

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Internet of Things (IoT) & Connected Devices · Depth: Expert, extended

Summary

The Cluster Aggregated GAN (CAG) framework is a hybrid generative approach designed to synthesize realistic appliance load patterns, addressing the limitations of existing GAN-based methods that treat all devices uniformly. CAG routes each appliance to a specialized branch based on its behavioral characteristics, distinguishing between intermittent and continuous loads. For intermittent appliances, a clustering module groups similar activation patterns, allocating dedicated CNN-based generators for each cluster to model both common and rare operational modes. Continuous appliances utilize a separate branch with an LSTM-based generator to capture gradual temporal evolution, maintaining training stability through sequence compression. A shared discriminator enforces realism across branches. Experiments on the UVIC smart plug dataset demonstrate CAG's superior performance, achieving a mean error of 8.03, standard deviation error of 13.46, fidelity RMSE of 42.6, period MAE of 23.1, Feature FID of 5.82e16, diversity RMSE of 1.34e2, cluster coverage of 3.03e-1, and cluster JS of 6.57e-1, significantly outperforming baselines.

Key takeaway

For AI Engineers developing synthetic data generators for Non-Intrusive Load Monitoring (NILM), you should adopt a hybrid approach like CAG. By classifying appliance behaviors and clustering distinct operational patterns, you can significantly improve the realism and diversity of generated load profiles. This strategy prevents mode collapse and enhances training stability, yielding more reliable synthetic datasets for algorithm development and privacy-preserving energy research. Consider implementing specialized generative branches for different data characteristics.

Key insights

Appliance load pattern generation benefits from specialized GAN branches tailored to device behavioral characteristics and clustered operational modes.

Principles

• Decompose generation tasks by device characteristics.
• Allocate dedicated modeling capacity for rare patterns.
• Use shared discriminators for consistent realism.

Method

CAG classifies appliances into intermittent/continuous, clusters intermittent patterns for CNN-GANs, and uses LSTM-GANs for continuous loads, all with a shared discriminator.

In practice

• Apply behavioral routing to heterogeneous time series data.
• Cluster similar patterns to prevent mode collapse in GANs.
• Downsample long sequences for stable LSTM-GAN training.

Topics

• Generative Adversarial Networks
• Non-Intrusive Load Monitoring
• Synthetic Data Generation
• Appliance Load Patterns
• Time Series Clustering
• LSTM GANs

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.