Abstract
Load forecasting is an important machine learning problem in the field of power system, which is of great significance to power system source load balance, power supply planning and system maintenance. With the development of power technology, fine-grained load data become more easily available, which puts forward new requirements for load forecasting accuracy. In this paper, we use a deep learning framework, namely conditional autoencoder, to forecast day-ahead load. And the conditional autoencoder extracts the characteristics of load time series through deep neural network and finds the sequence pattern of historical data. In addition, we use the load data of different time scales for training, so as to improve the accuracy of the model. In this paper, the real data of regenerative electric boiler in Jilin Province are used for load forecasting, and the effectiveness of the model is verified.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Deb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W.: A review on time series forecasting techniques for building energy consumption. Renew. Sustain. Energy Rev. 74(February), 902–924 (2017)
Xia, D., Wang, B., Li, H., Li, Y., Zhang, Z.: A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting. Neurocomputing 179, 246–263 (2016)
Rezaeian-Zadeh, M., Zand-Parsa, S., Abghari, H., Zolghadr, M., Singh, V.P.: Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions. Theor. Appl. Climatol. 109(3–4), 519–528 (2012)
Shen, F., Liu, J., Wu, K.: Multivariate time series forecasting based on elastic net and high-order fuzzy cognitive maps: a case study on human action prediction through EEG signals. IEEE Trans. Fuzzy Syst. 6706(c), 1–13 (2020)
Niu, T., Wang, J., Lu, H., Yang, W., Du, P.: Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting. Expert Syst. Appl. 148,113237 (2020).
Javeed Nizami, S., Al-Garni, A.Z.: Forecasting electric energy consumption using neural networks. Energy Policy 23(12), 1097–1104 (1995)
Cheng, J., Huang, K., Zheng, Z.: Towards better forecasting by fusing near and distant future visions. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence. pp. 3593–3600 (2020)
Suganthi, L., Samuel, A.A.: Energy models for demand forecasting - A review. Renew. Sustain. Energy Rev. 16(2), 1223–1240 (2012)
Sohn, K., Yan, X., Lee, H.: Learning structured output representation using deep conditional generative models. In: Advances in Neural Information Processing Systems. pp. 3483–3491 (2015)
Rhodes, J.D., Cole, W.J., Upshaw, C.R., Edgar, T.F., Webber, M.E.: Clustering analysis of residential electricity demand profiles. Appl. Energy. 135(2014), 461–471 (2014)
Jain, R.K., Smith, K.M., Culligan, P.J., Taylor, J.E.: Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Appl. Energy. 123, 168–178 (2014)
Chen, P., Liu, S., Shi, C., Hooi, B., Wang, B., Cheng, X.: NeUCAST: seasonal neural forecast of power grid time series. IJCAI International Joint Conference Artificial Intelligence, July 2018, pp. 3315–3321 (2018)
Wu, L., Kong, C., Hao, X., Chen, W.: A short-term load forecasting method based on GRU-CNN hybrid neural network model. Math. Probl. Eng. 2020, 1428104 (2020)
Tan, M., Yuan, S., Li, S., Su, Y., Li, H., He, F.H.: Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning. IEEE Trans. Power Syst. 35(4), 2937–2948 (2020)
Pesteie, M., Abolmaesumi, P., Rohling, R.: Adaptive augmentation of medical data using independently conditional variational auto-encoders. IEEE Trans. Med. Imaging 38(12), 2807–2820 (2019)
Zhao, Y., Hao, K., Tang, X.S., Chen, L., Wei, B.: A conditional variational autoencoder based self-transferred algorithm for imbalanced classification. Knowl. Based Syst. 128(9), 106756 (2021)
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings, pp. 1–14 (2014)
Acknowledgement
This work was financially supported by State Grid Technology Project (522300190009): Research and application of the key technologies of source-demand participation in market transactions based on data value discovery.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Qiu, Y., Sun, Y., Liu, C., Li, B., Wang, S., Peng, T. (2021). Aggregate Model for Power Load Forecasting Based on Conditional Autoencoder. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-84529-2_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84528-5
Online ISBN: 978-3-030-84529-2
eBook Packages: Computer ScienceComputer Science (R0)