Search for: All records

Non-Intrusive Load Monitoring (NILM) remains a critical issue in both commercial and residential energy management, with a key challenge being the requirement for individual appliance-specific deep learning models. These models often disregard the interconnected nature of loads and usage patterns, stemming from diverse user behavior. To address this, we introduce GraphNILM, an innovative end-to-end model that leverages graph neural networks to deliver appliance-level energy usage analysis for an entire home. In its initial phase, GraphNILM employs Gaussian random variables to depict the graph edges, later enhancing prediction accuracy by substituting these edges with observations of appliance interrelationships, stripping the individual load enery from the aggregated main energy all at one time, resulting in reduced memory usage, especially with more than three loads involved, thus presenting a time and space-efficient solution for real-world implementation. Comprehensive testing on popular NILM datasets confirms that our model outperforms existing benchmarks in both accuracy and memory consumption, suggesting its considerable promise for future deployment in edge devices.