Creating Temporally Correlated High-Resolution Profiles of Load Injection Using Constrained Generative Adversarial Networks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866909301772124160 |
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| author | Shah, Hritik Gopal Azimian, Behrouz Pal, Anamitra |
| author_facet | Shah, Hritik Gopal Azimian, Behrouz Pal, Anamitra |
| contents | Traditional smart meters, which measure energy usage every 15 minutes or more and report it at least a few hours later, lack the granularity needed for real-time decision-making. To address this practical problem, we introduce a new method using generative adversarial networks (GAN) that enforces temporal consistency on its high-resolution outputs via hard inequality constraints using convex optimization. A unique feature of our GAN model is that it is trained solely on slow timescale aggregated historical energy data obtained from smart meters. The results demonstrate that the model can successfully create minute-by-minute temporally correlated profiles of power usage from 15-minute interval average power consumption information. This innovative approach, emphasizing inter-neuron constraints, offers a promising avenue for improved high-speed state estimation in distribution systems and enhances the applicability of data-driven solutions for monitoring and subsequently controlling such systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_12166 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Creating Temporally Correlated High-Resolution Profiles of Load Injection Using Constrained Generative Adversarial Networks Shah, Hritik Gopal Azimian, Behrouz Pal, Anamitra Signal Processing Machine Learning Traditional smart meters, which measure energy usage every 15 minutes or more and report it at least a few hours later, lack the granularity needed for real-time decision-making. To address this practical problem, we introduce a new method using generative adversarial networks (GAN) that enforces temporal consistency on its high-resolution outputs via hard inequality constraints using convex optimization. A unique feature of our GAN model is that it is trained solely on slow timescale aggregated historical energy data obtained from smart meters. The results demonstrate that the model can successfully create minute-by-minute temporally correlated profiles of power usage from 15-minute interval average power consumption information. This innovative approach, emphasizing inter-neuron constraints, offers a promising avenue for improved high-speed state estimation in distribution systems and enhances the applicability of data-driven solutions for monitoring and subsequently controlling such systems. |
| title | Creating Temporally Correlated High-Resolution Profiles of Load Injection Using Constrained Generative Adversarial Networks |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2311.12166 |