Skip to main content
SpringerLink
Log in

Residential Short-Term Load Forecasting via Meta Learning and Domain Augmentation

  • Conference paper
  • First Online:
Artificial Intelligence for Knowledge Management, Energy, and Sustainability (AI4KMES 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 637))

  • 518 Accesses

Abstract

With the increasing adoption of electric devices and renewable energy generation, electric load forecasting, especially short-term load forecasting (STLF), has recently attracted more attention. Accurate short-term load forecasting is of significant importance for the safe and efficient operation of power grids. Deep learning-based models have achieved impressive success on several applications, including short-term load forecasting. Yet, most deep learning models do require a large amount of training data. However, in the real world, it may be very difficult or even impossible to collect enough data to train a reliable machine learning model. This makes is hard to adopt deep models for several real-world scenarios. Thus, it will be very helpful if deep learning models can be learned to tackle tasks with limited amount of training data and unseen tasks. In this work, we propose to use the meta-learning framework to train a long short-term memory-based model for short-term residential load forecasting. Specifically, by minimizing the task-level loss (loss over several tasks), the model is trained to perform well on different tasks. We also use domain randomization techniques to further augment the training tasks, which may further improve the generalization ability of the proposed model. Our model is evaluated on real-world data sets and compared against some classic forecasting models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Figure is from [42].

References

  1. Hahn, H., Meyer-Nieberg, S., Pickl, S.: Electric load forecasting methods: tools for decision making. Eur. J. Oper. Res. 199(3), 902–907 (2009)

    Article  Google Scholar 

  2. Wu, D.: Machine Learning Algorithms and Applications for Sustainable Smart Grid. McGill University (Canada) (2018)

    Google Scholar 

  3. Wu, D., Zeng, H., Lu, C., Boulet, B.: Two-stage energy management for office buildings with workplace EV charging and renewable energy. IEEE Trans. Transp. Electrification 3(1), 225–237 (2017)

    Article  Google Scholar 

  4. Bunn, D., Farmer, E.D.: Comparative models for electrical load forecasting (1985)

    Google Scholar 

  5. Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. Power Syst. 16(1), 44–55 (2001)

    Article  Google Scholar 

  6. Hall, D., Lutsey, N.: Electric vehicle charging guide for cities. Consulting Report (2020)

    Google Scholar 

  7. Wu, D., Zeng, H., Boulet, B.: Neighborhood level network aware electric vehicle charging management with mixed control strategy. In: 2014 IEEE International Electric Vehicle Conference (IEVC), pp. 1–7. IEEE (2014)

    Google Scholar 

  8. Wu, D., Rabusseau, G., François-lavet, V., Precup, D., Boulet, B.: Optimizing home energy management and electric vehicle charging with reinforcement learning. In: Proceedings of the 16th Adaptive Learning Agents (2018)

    Google Scholar 

  9. Dang, Q., Wu, D., Boulet, B.: EV charging management with ANN-based electricity price forecasting. In: 2020 IEEE Transportation Electrification Conference and Expo (ITEC), pp. 626–630. IEEE (2020)

    Google Scholar 

  10. Dang, Q., Wu, D., Boulet, B.: An advanced framework for electric vehicles interaction with distribution grids based on q-learning. In: 2019 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 3491–3495. IEEE (2019)

    Google Scholar 

  11. Dang, Q., Wu, D., Boulet, B.: A q-learning based charging scheduling scheme for electric vehicles. In: 2019 IEEE Transportation Electrification Conference and Expo (ITEC), pp. 1–5. IEEE (2019)

    Google Scholar 

  12. Dang, Q., Wu, D., Boulet, B.: EV fleet batteries as distributed energy resources considering dynamic electricity pricing. In: 2021 IEEE 12th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), pp. 1–6. IEEE (2021)

    Google Scholar 

  13. Adebayo, T.S., et al.: Modeling the dynamic linkage between renewable energy consumption, globalization, and environmental degradation in South Korea: does technological innovation matter? Energies 14(14), 4265 (2021)

    Article  Google Scholar 

  14. Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J.: Arima models to predict next-day electricity prices. IEEE Trans. Power Syst. 18(3), 1014–1020 (2003)

    Article  Google Scholar 

  15. He, H., Liu, T., Chen, R., Xiao, Y., Yang, J.: High frequency short-term demand forecasting model for distribution power grid based on ARIMA. In: IEEE CSAE, vol. 3, (Zhangjiaji, China), pp. 293–297 (2012)

    Google Scholar 

  16. Matsila, H., Bokoro, P.: Load forecasting using statistical time series model in a medium voltage distribution network. In: IEEE IECON, (Washington, DC), pp. 4974–4979 (2018)

    Google Scholar 

  17. Wu, D., Wang, B., Precup, D., Boulet, B.: Multiple kernel learning-based transfer regression for electric load forecasting. IEEE Trans. Smart Grid 11(2), 1183–1192 (2019)

    Article  Google Scholar 

  18. Ye, J., Yang, L.: A comparative study of ensemble support vector regression methods for short-term load forecasting. In: IEEE ICSAI, (Nanjing, China), pp. 139–143 (2018)

    Google Scholar 

  19. Chen, Y., et al.: Short-term electrical load forecasting using the support vector regression (SVR) model to calculate the demand response baseline for office buildings. Appl. Energy 195, 659–670 (2017)

    Article  Google Scholar 

  20. Dong, Y., Zhang, Z., Hong, W.-C.: A hybrid seasonal mechanism with a chaotic cuckoo search algorithm with a support vector regression model for electric load forecasting. Energies 11(4), 1009 (2018)

    Article  Google Scholar 

  21. Wu, D., Wang, B., Precup, D., Boulet, B.: Boosting based multiple kernel learning and transfer regression for electricity load forecasting. In: Altun, Y., et al. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10536, pp. 39–51. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71273-4_4

    Chapter  Google Scholar 

  22. Lin, W., Wu, D., Boulet, B.: Spatial-temporal residential short-term load forecasting via graph neural networks. IEEE Trans. Smart Grid 12(6), 5373–5384 (2021)

    Article  Google Scholar 

  23. Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y.: Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid 10(1), 841–851 (2019)

    Article  Google Scholar 

  24. Kim, N., Kim, M., Choi, J.K.: LSTM based short-term electricity consumption forecast with daily load profile sequences. In: IEEE GCCE, Las Vegas, pp. 136–137 (2018)

    Google Scholar 

  25. Moon, J., Kim, Y., Son, M., Hwang, E.: Hybrid short-term load forecasting scheme using random forest and multilayer perceptron. Energies 11(12), 3283 (2018)

    Article  Google Scholar 

  26. Son, M., Moon, J., Jung, S., Hwang, E.: A short-term load forecasting scheme based on auto-encoder and random forest. In: Ntalianis, K., Vachtsevanos, G., Borne, P., Croitoru, A. (eds.) APSAC 2018. LNEE, vol. 574, pp. 138–144. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21507-1_21

    Chapter  Google Scholar 

  27. Hoens, T.R., Polikar, R., Chawla, N.V.: Learning from streaming data with concept drift and imbalance: an overview. Progress Artif. Intell. 1(1), 89–101 (2012)

    Article  Google Scholar 

  28. Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126–1135. PMLR (2017)

    Google Scholar 

  29. Rusu, A.A., et al.: Meta-learning with latent embedding optimization. arXiv preprint arXiv:1807.05960 (2018)

  30. Zintgraf, L., Shiarli, K., Kurin, V., Hofmann, K., Whiteson, S.: Fast context adaptation via meta-learning. In: International Conference on Machine Learning, pp. 7693–7702. PMLR (2019)

    Google Scholar 

  31. Olier, I., et al.: Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. Mach. Learn. 107(1), 285–311 (2018)

    Article  MathSciNet  Google Scholar 

  32. Mireshghallah, F., Shrivastava, V., Shokouhi, M., Berg-Kirkpatrick, T., Sim, R., Dimitriadis, D.: UserIdentifier: implicit user representations for simple and effective personalized sentiment analysis. arXiv preprint arXiv:2110.00135 (2021)

  33. Hospedales, T., Antoniou, A., Micaelli, P., Storkey, A.: Meta-learning in neural networks: a survey. arXiv preprint arXiv:2004.05439 (2020)

  34. Giraud-Carrier, C., Vilalta, R., Brazdil, P.: Introduction to the special issue on meta-learning. Mach. Learn. 54(3), 187–193 (2004)

    Article  Google Scholar 

  35. Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199–1208 (2018)

    Google Scholar 

  36. Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. arXiv preprint arXiv:1703.05175 (2017)

  37. Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: One-shot learning with memory-augmented neural networks. arXiv preprint arXiv:1605.06065 (2016)

  38. Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: International Conference on Machine Learning, pp. 1842–1850. PMLR (2016)

    Google Scholar 

  39. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  40. Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)

  41. Bengio, Y., Simard, P., Frasconi, P., et al.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Networks 5(2), 157–166 (1994)

    Article  Google Scholar 

  42. Rassem, A., El-Beltagy, M., Saleh, M.: Cross-country skiing gears classification using deep learning. arXiv preprint arXiv:1706.08924 (2017)

  43. Zang, X., Yao, H., Zheng, G., Xu, N., Xu, K., Li, Z.: MetaLight: value-based meta-reinforcement learning for traffic signal control. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 1153–1160 (2020)

    Google Scholar 

  44. Khodadadeh, S., Bölöni, L., Shah, M.: Unsupervised meta-learning for few-shot image classification. arXiv preprint arXiv:1811.11819 (2018)

  45. Yin, W.: Meta-learning for few-shot natural language processing: a survey. arXiv preprint arXiv:2007.09604 (2020)

Download references

Author information

Authors and Affiliations

  1. McGill University, Montreal, QC, H3A 0E9, Canada

    Di Wu & Benoit Boulet

  2. Google LLC, New York, NY, 10044, USA

    Can Cui

Authors
  1. Di Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Can Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benoit Boulet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Di Wu .

Editor information

Editors and Affiliations

  1. University of Reims Champagne-Ardenne, Reims, France

    Eunika Mercier-Laurent

  2. Istanbul Technical University, Istanbul, Turkey

    Gülgün Kayakutlu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wu, D., Cui, C., Boulet, B. (2022). Residential Short-Term Load Forecasting via Meta Learning and Domain Augmentation. In: Mercier-Laurent, E., Kayakutlu, G. (eds) Artificial Intelligence for Knowledge Management, Energy, and Sustainability. AI4KMES 2021. IFIP Advances in Information and Communication Technology, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-030-96592-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-96592-1_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96591-4

  • Online ISBN: 978-3-030-96592-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation