| Autor: |
Jaradat A; The Department of Computer Science, Western University, London, ON N6A 3K7, Canada., Alarbi M; The Department of Computer Science, Western University, London, ON N6A 3K7, Canada., Haque A; The Department of Computer Science, Western University, London, ON N6A 3K7, Canada., Lutfiyya H; The Department of Computer Science, Western University, London, ON N6A 3K7, Canada. |
| Jazyk: |
angličtina |
| Zdroj: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Aug 29; Vol. 24 (17). Date of Electronic Publication: 2024 Aug 29. |
| DOI: |
10.3390/s24175619 |
| Abstrakt: |
Realistic appliance power consumption data are essential for developing smart home energy management systems and the foundational algorithms that analyze such data. However, publicly available datasets are scarce and time-consuming to collect. To address this, we propose HYDROSAFE, a hybrid deterministic-probabilistic model designed to generate synthetic appliance power consumption profiles. HYDROSAFE employs the Median Difference Test (MDT) for profile characterization and the Density and Dynamic Time Warping based Spatial Clustering for appliance operation modes (DDTWSC) algorithm to cluster appliance usage according to the corresponding Appliance Operation Modes (AOMs). By integrating stochastic methods, such as white noise, switch-on surge, ripples, and edge position components, the model adds variability and realism to the generated profiles. Evaluation using a normalized DTW-distance matrix shows that HYDROSAFE achieves high fidelity, with an average DTW distance of ten samples at a 1Hz sampling frequency, demonstrating its effectiveness in producing synthetic datasets that closely mimic real-world data. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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