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Residential buildings constitute roughly one-fourth of the total energy use across the globe. Numerous studies have shown that providing an energy breakdown increases residents' awareness of energy use and can help save up to 15% energy. A significant amount of prior work has looked into source-separation techniques collectively called non-intrusive load monitoring (NILM), and most prior NILM research has leveraged high-frequency household aggregate data for energy breakdown. However, in practice most smart meters only sample hourly or once every 15 minutes, and existing NILM techniques show poor performance at such a low sampling rate. In this paper, we propose a TreeCNN model for energy breakdown on low frequency data. There are three key insights behind the design of our model: i) households consume energy with regular temporal patterns, which can be well captured by filters learned in CNNs; ii) tree structure isolates the pattern learning of each appliance that helps avoid magnitude variance problem, while preserves relationship among appliances; iii) tree structure enables the separation of known appliance from unknown ones, which de-noises the input time series for better appliance-level reconstruction. Our TreeCNN model outperformed seven existing baselines on a public benchmark dataset with lower estimation error and higher accuracy on detecting the active states of appliances.
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GraphNILM: A Graph Neural Network for Energy Disaggregation
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.
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- Award ID(s):
- 2153369
- PAR ID:
- 10505936
- Publisher / Repository:
- Springer Nature Singapore
- Date Published:
- Journal Name:
- Pacific-Asia Conference on Knowledge Discovery and Data Mining
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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