Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model Approach
With the growing global emphasis on environmental protection policies, renewable energy generation systems have become widely adopted. In particular, photovoltaic (PV) systems have gained popularity for their ease of management, while digital twin (DT) systems are being developed to enable real-time...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10824779/ |
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| author | Jooseung Lee Jimyung Kang Sangwoo Son Hui-Myoung Oh |
| author_facet | Jooseung Lee Jimyung Kang Sangwoo Son Hui-Myoung Oh |
| author_sort | Jooseung Lee |
| collection | DOAJ |
| description | With the growing global emphasis on environmental protection policies, renewable energy generation systems have become widely adopted. In particular, photovoltaic (PV) systems have gained popularity for their ease of management, while digital twin (DT) systems are being developed to enable real-time monitoring and management of the systems. Furthermore, the DT systems simulate the operations of the physical systems in real-time based on the data collected from various sensors. To this end, a novel sensor data generation model based on numerical weather prediction (NWP) data is proposed to forecast the future operations of PV systems using DT systems. The proposed model utilizes a hybrid data-driven model structure combining supervised learning-based long short-term memory (LSTM) and unsupervised learning-based generative adversarial network (GAN) to enhance both average and variance accuracy. Specifically, TransTimeGAN is proposed, which combines TimeGAN with Transformer to effectively capture 15-min variability. For practical applicability, the proposed model is trained and validated using data from a self-developed PV DT system. To evaluate the effectiveness of the proposed model, the similarities between normalized real and generated data are compared using a range of error metrics and statistical metrics. For representative error metrics, the proposed model achieves a mean squared error (MSE) of 7.84e-3 and a dynamic time warping (DTW) score of 1.3769. Regarding representative statistical metrics, the model achieves a Kullback-Leibler divergence (KLD, max-normalized) of 0.9591 and a standard deviation similarity (SDS) of 0.9671. The experimental results demonstrate that the proposed model delivers superior performance in generating data compared with various data-driven models across a range of numerical metrics and visual assessments. |
| format | Article |
| id | doaj-art-dfad23e8d2504137885994af87b3fe84 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-dfad23e8d2504137885994af87b3fe842025-01-14T00:02:16ZengIEEEIEEE Access2169-35362025-01-01135009502210.1109/ACCESS.2025.352565910824779Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model ApproachJooseung Lee0https://orcid.org/0009-0003-2070-1496Jimyung Kang1https://orcid.org/0000-0002-5922-4434Sangwoo Son2https://orcid.org/0009-0009-9566-6123Hui-Myoung Oh3https://orcid.org/0009-0003-5521-2959Power Grid Research Division, Korea Electrotechnology Research Institute, Ansan, South KoreaPower Grid Research Division, Korea Electrotechnology Research Institute, Ansan, South KoreaPower Grid Research Division, Korea Electrotechnology Research Institute, Ansan, South KoreaPower Grid Research Division, Korea Electrotechnology Research Institute, Ansan, South KoreaWith the growing global emphasis on environmental protection policies, renewable energy generation systems have become widely adopted. In particular, photovoltaic (PV) systems have gained popularity for their ease of management, while digital twin (DT) systems are being developed to enable real-time monitoring and management of the systems. Furthermore, the DT systems simulate the operations of the physical systems in real-time based on the data collected from various sensors. To this end, a novel sensor data generation model based on numerical weather prediction (NWP) data is proposed to forecast the future operations of PV systems using DT systems. The proposed model utilizes a hybrid data-driven model structure combining supervised learning-based long short-term memory (LSTM) and unsupervised learning-based generative adversarial network (GAN) to enhance both average and variance accuracy. Specifically, TransTimeGAN is proposed, which combines TimeGAN with Transformer to effectively capture 15-min variability. For practical applicability, the proposed model is trained and validated using data from a self-developed PV DT system. To evaluate the effectiveness of the proposed model, the similarities between normalized real and generated data are compared using a range of error metrics and statistical metrics. For representative error metrics, the proposed model achieves a mean squared error (MSE) of 7.84e-3 and a dynamic time warping (DTW) score of 1.3769. Regarding representative statistical metrics, the model achieves a Kullback-Leibler divergence (KLD, max-normalized) of 0.9591 and a standard deviation similarity (SDS) of 0.9671. The experimental results demonstrate that the proposed model delivers superior performance in generating data compared with various data-driven models across a range of numerical metrics and visual assessments.https://ieeexplore.ieee.org/document/10824779/Deep learningdigital twinenergy AIgenerative adversarial network (GAN)photovoltaicrenewable energy |
| spellingShingle | Jooseung Lee Jimyung Kang Sangwoo Son Hui-Myoung Oh Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model Approach IEEE Access Deep learning digital twin energy AI generative adversarial network (GAN) photovoltaic renewable energy |
| title | Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model Approach |
| title_full | Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model Approach |
| title_fullStr | Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model Approach |
| title_full_unstemmed | Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model Approach |
| title_short | Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model Approach |
| title_sort | numerical weather data driven sensor data generation for pv digital twins a hybrid model approach |
| topic | Deep learning digital twin energy AI generative adversarial network (GAN) photovoltaic renewable energy |
| url | https://ieeexplore.ieee.org/document/10824779/ |
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