Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, Cilt: 3 Sayı: 2, 141 - 158, 30.06.2023
https://doi.org/10.53391/mmnsa.1320914

Öz

Kaynakça

  • Moon, J., Jung, S., Park, S. and Hwang, E. Conditional tabular GAN-based two-stage data generation scheme for short-term load forecasting. IEEE Access, 8, 205327-205339, (2020).
  • Arjovsky, M., Chintala, S. and Bottou, L. Wasserstein generative adversarial networks. In Proceedings, 34th International Conference On Machine Learning (PMLR), (Vol. 70), pp. 214-223, (2017, July).
  • Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V. and Courville, A. Improved training of wasserstein gans. In NeurIPS Proceedings, Advances in Neural Information Processing Systems 30, (2017).
  • Yu, L., Zhang, W., Wang, J. and Yu, Y. SeqGAN: Sequence generative adversarial nets with policy gradient. In Proceedings, Thirty-First AAAI Conference On Artificial Intelligence, (Vol. 31, No. 1), pp. 2852-2858, (2017, February).
  • Zhu, J.Y., Park, T., Isola, P. and Efros, A.A. Unpaired image-to-image translation using cycleconsistent adversarial networks. In Proceedings, IEEE International Conference on Computer Vision (ICCV), pp. 2223-2232, (2017, October).
  • Bendaoud, N., Farah, N. and Ben Ahmed, S. Comparing generative adversarial networks architectures for electricity demand forecasting. Energy and Buildings, 247, 111152, (2021).
  • Silva, V.L., Heaney, C.E., Li, Y. and Pain, C.C. Data assimilation predictive GAN (DAPredGAN) applied to a spatio-temporal compartmental model in epidemiology. Journal of Scientific Computing, 94(1), 25, (2023).
  • Esteban, C., Hyland, S. and Rätsch, G. Real-valued (medical) time series generation with recurrent conditional gans. ArXiv Prints, ArXiv:1706.02633, (2017).
  • Yoon, J., Jarrett, D. and Van der Schaar, M. Time-series generative adversarial networks. Advances In Neural Information Processing Systems, 32, (2019).
  • Yilmaz, B. and Korn, R. Synthetic demand data generation for individual electricity consumers: generative adversarial networks (GANs). Energy and AI, 9, 100161, (2022).
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S. et al. Generative adversarial nets. In NeurIPS Proceedings, Advances in Neural Information Processing Systems 27, (2014).
  • Ramasinghe, S., Khan, S., Barnes, N. and Gould, S. Spectral-GANs for high-resolution 3D point-cloud generation. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8169-8176, Las Vegas, NV, USA, (2020, October).
  • Wang, C., Wang, C., Xu, C. and Tao, D. Tag disentangled generative adversarial networks for object image re-rendering. In International Joint Conference On Artificial Intelligence (IJCAI), Melbourne, Australia, (2017, August).
  • Tan, W.R., Chan, C.S., Aguirre, H.E. and Tanaka, K. ArtGAN: Artwork synthesis with conditional categorical GANs. In 2017 IEEE International Conference on Image Processing (ICIP), pp. 3760-3764, Beijing, China, (2017, September).
  • Merrigan, A. and Smeaton, A.F. Using a GAN to generate adversarial examples to facial image recognition. ArXiv Preprint, ArXiv: 2111.15213, (2021).
  • Engel, J., Agrawal, K., Chen, S., Gulrajani, I., Donahue, C. and Roberts, A. Gansynth: Adversarial neural audio synthesis. ArXiv Preprint, ArXiv: 1902.08710, (2019).
  • Donahue, C., McAuley, J. and Puckette, M. Adversarial audio synthesis. ArXiv Preprint, ArXiv: 1802.04208, (2018).
  • Young, T., Hazarika, D., Poria, S. and Cambria, E. Recent trends in deep learning based natural language processing. IEEE Computational Intelligence Magazine, 13(3), 55-75, (2018).
  • Englund, C., Aksoy, E.E., Alonso-Fernandez, F., Cooney, M.D., Pashami, S. and Astrand, B. AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control. Smart Cities, 4(2), 783-802, (2021).
  • Wiese, M., Knobloch, R., Korn, R. and Kretschmer, P. Quant GANs: deep generation of financial time series. Quantitative Finance, 20(9), 1419-1440, (2020).
  • Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z. and Smolley, S.P. Least squares generative adversarial networks. In Proceedings, IEEE International Conference on Computer Vision (ICCV), pp. 2794-2802, (2017, April).
  • Mirza, M. and Osindero, S. Conditional generative adversarial nets. ArXiv Preprint, ArXiv: 1411.1784, (2014).
  • Li, J., Wang, X., Lin, Y., Sinha, A. and Wellman, M. Generating realistic stock market order streams. In Proceedings, Conference on Artificial Intelligence (AAAI), (Vol. 34, No. 01), pp. 727-734, (2020, April).
  • Zhang, K., Zhong, G., Dong, J., Wang, S. and Wang, Y. Stock market prediction based on generative adversarial network. Procedia Computer Science, 147, 400-406, (2019).
  • Koshiyama, A., Firoozye, N. and Treleaven, P. Generative adversarial networks for financial trading strategies fine-tuning and combination. Quantitative Finance, 21(5), 797-813, (2021).
  • Chen, Y., Wang, Y., Kirschen, D. and Zhang, B. Model-free renewable scenario generation using generative adversarial networks. IEEE Transactions on Power Systems, 33(3), 3265-3275, (2018).
  • Li, J., Chen, Z., Cheng, L. and Liu, X. Energy data generation with wasserstein deep convolutional generative adversarial networks. Energy, 257, 124694, (2022).
  • Du, Z., Chen, K., Chen, S., He, J., Zhu, X. and Jin, X. Deep learning GAN-based data generation and fault diagnosis in the data center HVAC system. Energy and Buildings, 289, 113072, (2023).
  • Wang, Z. and Hong, T. Generating realistic building electrical load profiles through the generative adversarial network (GAN). Energy and Buildings, 224, 110299, (2020).
  • Perera, A.T.D., Khayatian, F., Eggimann, S., Orehounig, K. and Halgamuge, S. Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs. Applied Energy, 328, 120169, (2022).
  • Dong, W., Chen, X. and Yang, Q. Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability. Applied Energy, 308, 118387, (2022).
  • Chen, Z., Li, J., Cheng, L. and Liu, X. Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation. Applied Energy, 334, 120711, (2023).
  • Ye, Y., Strong, M., Lou, Y., Faulkner, C.A., Zuo, W. and Upadhyaya, S. Evaluating performance of different generative adversarial networks for large-scale building power demand prediction. Energy And Buildings, 269, 112247, (2022).
  • Sözen, A. and Arcaklioglu, E. Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy, 35(10), 4981-4992, (2007).
  • Hamzaçebi, C. Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy, 35(3), 2009-2016, (2007).
  • Kankal, M., Akpınar, A., Kömürcü, M.I. and Öz¸sahin, T.¸S. Modeling and forecasting of ˙ Turkey’s energy consumption using socio-economic and demographic variables. Applied Energy, 88(5), 1927-1939, (2011).
  • Es, H.A., Kalender Öksüz, F.Y. and Hamzacebi, C. Forecasting the net energy demand of Turkey by artificial neural networks. Journal of The Faculty of Engineering and Architecture of Gazi University, 29(3), (2014).
  • Ağbulut, Ü. Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustainable Production and Consumption, 29, 141-157, (2022).
  • Yasin Çodur, M. and Ünal, A. An estimation of transport energy demand in Turkey via artificial neural networks. Promet-Traffic & Transportation, 31(2), 151-161, (2019).
  • Yilmaz, B. Understanding the mathematical background of generative adversarial neural networks (GANs). Available At SSRN 3981773, (2021).
  • Wang, Y. A mathematical introduction to generative adversarial nets (GAN). ArXiv Preprint, ArXiv: 2009.00169, (2020).
  • Ni, H., Szpruch, L., Wiese, M., Liao, S. and Xiao, B. Conditional sig-wasserstein GANs for time series generation. ArXiv Preprint, ArXiv: 2006.05421, (2020).
  • Yukseltan, E., Kok, A., Yucekaya, A., Bilge, A., Aktunc, E.A. and Hekimoglu, M. The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey. Utilities Policy, 76, 101359, (2022).
  • Kingma, D.P. and Ba, J. Adam: A method for stochastic optimization. ArXiv Preprint, ArXiv: 1412.6980, (2017).
  • Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B. and Hochreiter, S. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS Proceedings, Advances in Neural Information Processing Systems 30, (2017).
  • Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G. et al. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS Proceedings, Advances in Neural Information Processing Systems 32, (2019).

Generative adversarial network for load data generation: Türkiye energy market case

Yıl 2023, Cilt: 3 Sayı: 2, 141 - 158, 30.06.2023
https://doi.org/10.53391/mmnsa.1320914

Öz

Load modeling is crucial in improving energy efficiency and saving energy sources. In the last decade, machine learning has become favored and has demonstrated exceptional performance in load modeling. However, their implementation heavily relies on the quality and quantity of available data. Gathering sufficient high-quality data is time-consuming and extremely expensive. Therefore, generative adversarial networks (GANs) have shown their prospect of generating synthetic data, which can solve the data shortage problem. This study proposes GAN-based models (RCGAN, TimeGAN, CWGAN, and RCWGAN) to generate synthetic load data. It focuses on Türkiye's electricity load and generates realistic synthetic load data. The educated synthetic load data can reduce prediction errors in load when combined with recorded data and enhance risk management calculations.

Kaynakça

  • Moon, J., Jung, S., Park, S. and Hwang, E. Conditional tabular GAN-based two-stage data generation scheme for short-term load forecasting. IEEE Access, 8, 205327-205339, (2020).
  • Arjovsky, M., Chintala, S. and Bottou, L. Wasserstein generative adversarial networks. In Proceedings, 34th International Conference On Machine Learning (PMLR), (Vol. 70), pp. 214-223, (2017, July).
  • Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V. and Courville, A. Improved training of wasserstein gans. In NeurIPS Proceedings, Advances in Neural Information Processing Systems 30, (2017).
  • Yu, L., Zhang, W., Wang, J. and Yu, Y. SeqGAN: Sequence generative adversarial nets with policy gradient. In Proceedings, Thirty-First AAAI Conference On Artificial Intelligence, (Vol. 31, No. 1), pp. 2852-2858, (2017, February).
  • Zhu, J.Y., Park, T., Isola, P. and Efros, A.A. Unpaired image-to-image translation using cycleconsistent adversarial networks. In Proceedings, IEEE International Conference on Computer Vision (ICCV), pp. 2223-2232, (2017, October).
  • Bendaoud, N., Farah, N. and Ben Ahmed, S. Comparing generative adversarial networks architectures for electricity demand forecasting. Energy and Buildings, 247, 111152, (2021).
  • Silva, V.L., Heaney, C.E., Li, Y. and Pain, C.C. Data assimilation predictive GAN (DAPredGAN) applied to a spatio-temporal compartmental model in epidemiology. Journal of Scientific Computing, 94(1), 25, (2023).
  • Esteban, C., Hyland, S. and Rätsch, G. Real-valued (medical) time series generation with recurrent conditional gans. ArXiv Prints, ArXiv:1706.02633, (2017).
  • Yoon, J., Jarrett, D. and Van der Schaar, M. Time-series generative adversarial networks. Advances In Neural Information Processing Systems, 32, (2019).
  • Yilmaz, B. and Korn, R. Synthetic demand data generation for individual electricity consumers: generative adversarial networks (GANs). Energy and AI, 9, 100161, (2022).
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S. et al. Generative adversarial nets. In NeurIPS Proceedings, Advances in Neural Information Processing Systems 27, (2014).
  • Ramasinghe, S., Khan, S., Barnes, N. and Gould, S. Spectral-GANs for high-resolution 3D point-cloud generation. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8169-8176, Las Vegas, NV, USA, (2020, October).
  • Wang, C., Wang, C., Xu, C. and Tao, D. Tag disentangled generative adversarial networks for object image re-rendering. In International Joint Conference On Artificial Intelligence (IJCAI), Melbourne, Australia, (2017, August).
  • Tan, W.R., Chan, C.S., Aguirre, H.E. and Tanaka, K. ArtGAN: Artwork synthesis with conditional categorical GANs. In 2017 IEEE International Conference on Image Processing (ICIP), pp. 3760-3764, Beijing, China, (2017, September).
  • Merrigan, A. and Smeaton, A.F. Using a GAN to generate adversarial examples to facial image recognition. ArXiv Preprint, ArXiv: 2111.15213, (2021).
  • Engel, J., Agrawal, K., Chen, S., Gulrajani, I., Donahue, C. and Roberts, A. Gansynth: Adversarial neural audio synthesis. ArXiv Preprint, ArXiv: 1902.08710, (2019).
  • Donahue, C., McAuley, J. and Puckette, M. Adversarial audio synthesis. ArXiv Preprint, ArXiv: 1802.04208, (2018).
  • Young, T., Hazarika, D., Poria, S. and Cambria, E. Recent trends in deep learning based natural language processing. IEEE Computational Intelligence Magazine, 13(3), 55-75, (2018).
  • Englund, C., Aksoy, E.E., Alonso-Fernandez, F., Cooney, M.D., Pashami, S. and Astrand, B. AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control. Smart Cities, 4(2), 783-802, (2021).
  • Wiese, M., Knobloch, R., Korn, R. and Kretschmer, P. Quant GANs: deep generation of financial time series. Quantitative Finance, 20(9), 1419-1440, (2020).
  • Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z. and Smolley, S.P. Least squares generative adversarial networks. In Proceedings, IEEE International Conference on Computer Vision (ICCV), pp. 2794-2802, (2017, April).
  • Mirza, M. and Osindero, S. Conditional generative adversarial nets. ArXiv Preprint, ArXiv: 1411.1784, (2014).
  • Li, J., Wang, X., Lin, Y., Sinha, A. and Wellman, M. Generating realistic stock market order streams. In Proceedings, Conference on Artificial Intelligence (AAAI), (Vol. 34, No. 01), pp. 727-734, (2020, April).
  • Zhang, K., Zhong, G., Dong, J., Wang, S. and Wang, Y. Stock market prediction based on generative adversarial network. Procedia Computer Science, 147, 400-406, (2019).
  • Koshiyama, A., Firoozye, N. and Treleaven, P. Generative adversarial networks for financial trading strategies fine-tuning and combination. Quantitative Finance, 21(5), 797-813, (2021).
  • Chen, Y., Wang, Y., Kirschen, D. and Zhang, B. Model-free renewable scenario generation using generative adversarial networks. IEEE Transactions on Power Systems, 33(3), 3265-3275, (2018).
  • Li, J., Chen, Z., Cheng, L. and Liu, X. Energy data generation with wasserstein deep convolutional generative adversarial networks. Energy, 257, 124694, (2022).
  • Du, Z., Chen, K., Chen, S., He, J., Zhu, X. and Jin, X. Deep learning GAN-based data generation and fault diagnosis in the data center HVAC system. Energy and Buildings, 289, 113072, (2023).
  • Wang, Z. and Hong, T. Generating realistic building electrical load profiles through the generative adversarial network (GAN). Energy and Buildings, 224, 110299, (2020).
  • Perera, A.T.D., Khayatian, F., Eggimann, S., Orehounig, K. and Halgamuge, S. Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs. Applied Energy, 328, 120169, (2022).
  • Dong, W., Chen, X. and Yang, Q. Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability. Applied Energy, 308, 118387, (2022).
  • Chen, Z., Li, J., Cheng, L. and Liu, X. Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation. Applied Energy, 334, 120711, (2023).
  • Ye, Y., Strong, M., Lou, Y., Faulkner, C.A., Zuo, W. and Upadhyaya, S. Evaluating performance of different generative adversarial networks for large-scale building power demand prediction. Energy And Buildings, 269, 112247, (2022).
  • Sözen, A. and Arcaklioglu, E. Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy, 35(10), 4981-4992, (2007).
  • Hamzaçebi, C. Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy, 35(3), 2009-2016, (2007).
  • Kankal, M., Akpınar, A., Kömürcü, M.I. and Öz¸sahin, T.¸S. Modeling and forecasting of ˙ Turkey’s energy consumption using socio-economic and demographic variables. Applied Energy, 88(5), 1927-1939, (2011).
  • Es, H.A., Kalender Öksüz, F.Y. and Hamzacebi, C. Forecasting the net energy demand of Turkey by artificial neural networks. Journal of The Faculty of Engineering and Architecture of Gazi University, 29(3), (2014).
  • Ağbulut, Ü. Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustainable Production and Consumption, 29, 141-157, (2022).
  • Yasin Çodur, M. and Ünal, A. An estimation of transport energy demand in Turkey via artificial neural networks. Promet-Traffic & Transportation, 31(2), 151-161, (2019).
  • Yilmaz, B. Understanding the mathematical background of generative adversarial neural networks (GANs). Available At SSRN 3981773, (2021).
  • Wang, Y. A mathematical introduction to generative adversarial nets (GAN). ArXiv Preprint, ArXiv: 2009.00169, (2020).
  • Ni, H., Szpruch, L., Wiese, M., Liao, S. and Xiao, B. Conditional sig-wasserstein GANs for time series generation. ArXiv Preprint, ArXiv: 2006.05421, (2020).
  • Yukseltan, E., Kok, A., Yucekaya, A., Bilge, A., Aktunc, E.A. and Hekimoglu, M. The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey. Utilities Policy, 76, 101359, (2022).
  • Kingma, D.P. and Ba, J. Adam: A method for stochastic optimization. ArXiv Preprint, ArXiv: 1412.6980, (2017).
  • Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B. and Hochreiter, S. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS Proceedings, Advances in Neural Information Processing Systems 30, (2017).
  • Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G. et al. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS Proceedings, Advances in Neural Information Processing Systems 32, (2019).
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sayısal ve Hesaplamalı Matematik (Diğer), Finansal Matematik
Bölüm Araştırma Makalesi
Yazarlar

Bilgi Yılmaz 0000-0002-9646-2757

Erken Görünüm Tarihi 30 Haziran 2023
Yayımlanma Tarihi 30 Haziran 2023
Gönderilme Tarihi 18 Nisan 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 3 Sayı: 2

Kaynak Göster

APA Yılmaz, B. (2023). Generative adversarial network for load data generation: Türkiye energy market case. Mathematical Modelling and Numerical Simulation With Applications, 3(2), 141-158. https://doi.org/10.53391/mmnsa.1320914


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