Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Aug 29;24(17):5619.
doi: 10.3390/s24175619.

HYDROSAFE: A Hybrid Deterministic-Probabilistic Model for Synthetic Appliance Profiles Generation

Abdelkareem Jaradat  1 , Muhamed Alarbi  1 , Anwar Haque  1 , Hanan Lutfiyya  1
Affiliations

HYDROSAFE: A Hybrid Deterministic-Probabilistic Model for Synthetic Appliance Profiles Generation

Abdelkareem Jaradat et al. Sensors (Basel). .

Abstract

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.

Keywords: HEMSs; SHEMSs; appliance operation modes; demand response (DR); dynamic time warping (DTW); load profile simulation.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Two SUPs for a clothes dryer. Each SUP is activated with a different AOM.
Figure 2
Figure 2
The annual power consumption [23] and corresponding costs for operating 3 appliances, and the potential savings by switching to the lighter operation modes.
Figure 3
Figure 3
The architecture of HYDROSAFE.
Figure 4
Figure 4
A square wave with uniform noise, moving average, and moving median.
Figure 5
Figure 5
The Euclidean distance between trimmed vs. smoothed multiple SUP for a dryer using moving median with variation in the window size.
Figure 6
Figure 6
(a) The smoothed SUP sequence ψ^(n) with the indicator vector Iψ^(n). (b) A zoom-in to a state showing its upper and lower bounds, the upper and lower bounds of thick edges, the exact edges.
Figure 7
Figure 7
The distance matrix, Δ, for 3 appliances in 2 houses [23]. (A) A dryer in house-2 with 3 AOMs. (B) A dryer in house-1 with 3 AOMs. (C) A clothes washer in house-1 with 2 AOMs. (D) A dishwasher in house-1 with 2 AOMs.
Figure 8
Figure 8
The impact of changing the noise coefficient, ξ, on the values of the distance mean, δ¯, for a dryer.
Figure 9
Figure 9
The impact of changing the SOS coefficient, ϑ, on the values of the distance mean, δ¯, for a dryer.
Figure 10
Figure 10
The impact of changing the ripple parameters, ρ and γ, on the values of the distance mean, δ¯, for a dryer.
Figure 11
Figure 11
The impact of EEP factor, ℓ, on the values of the distance mean, δ¯, for a dryer.
See this image and copyright information in PMC

References

    1. Ahmad T., Chen H. Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment. Energy. 2018;160:1008–1020. doi: 10.1016/j.energy.2018.07.084. - DOI
    1. Energy U. Annual Energy Review 2009. US Energy Information Administration; Washington, DC, USA: 2010. pp. 19–53.
    1. Canada Energy Regulator, Canada’s Energy Future. 2021. [(accessed on 29 May 2023)]. Available online: https://bit.ly/3d8vIqH.
    1. U.S. Energy Information Administration Annual Energy Outlook 2021. [(accessed on 29 May 2023)]; Available online: https://www.eia.gov/outlooks/aeo/
    1. Canada N.R. Natural Resources Canada, Appliances for Residential Use. 2021. [(accessed on 29 May 2023)]. Available online: https://bit.ly/3omEb0d.

LinkOut - more resources