Abstract
The traditional load prediction is based on linear modeling, which does not consider the uncertainty of load and has no small errors. Deep learning methods are used for load prediction because of their strong data feature extraction and fitting ability. From the accuracy of electric heating load prediction, an electric heating load prediction model based on TCN-LSTM hybrid neural network is proposed. Several influencing factors with the strongest correlation with electric heating load are selected as input features by Pearson correlation analysis among multiple features, and weakly correlated meteorological features are filtered out; then CEEMDAN decomposition is used to decompose the historical electric heating load time series into multiple eigenfunctions as well as a residual term to obtain the historical input feature time series corresponding to each input feature; finally, the TCN and LSTM hybrid models are finally used for electric heating load prediction. The simulation results show that the electric heating load prediction model based on the TCN-LSTM hybrid neural network can extract effective information from the historical load data, realize the dimensionality reduction processing, and improve the operation rate and accuracy of the artificial network.
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
Similar content being viewed by others
References
Huang Y, Zhu Y, Mu G et al (2018) Evaluation of adjustable capacity of household electrical heating load based on temperature forecast. Power Syst Technol 42(8):2487–2493 (in Chinese). https://doi.org/10.13335/j.1000-3673.pst.2018.0547
Yan G, Kan T, Yang Y et al (2020) Demand response optimal scheduling for distributed electric heating based on deep reinforcement learning. Power Syst Technol 44(11):4140–4149. https://doi.org/10.13335/j.1000-3673.pst.2020.0252a
Wang W, Li F (2020) Study on substitutable value of electric heating instead of coal heating in northern China under carbon constraints. J Clean Prod 260. https://doi.org/10.1016/j.jclepro.2020.121155
Lu J, Zhang Q, Yang Z et al (2019) Short-term load forecasting method based on CNN-LSTM hybrid neural network model. Autom Electr Power Syst 43(08):131–137. https://doi.org/10.7500/AEPS20181012004
Chen J, Yang J, Lou Z (2019) A new short-term load forecasting model based on XGBoost algorithm. Electr Meas Instrum 56(21):23–29. https://doi.org/10.19753/j.issn1001-1390.2019.021.004
Shen C, Qin J, Sheng W et al (2016) Study on short-term forecasting of distribution transformer load using wavelet and clustering method. Power Syst Technol 40(02):521–526. https://doi.org/10.13335/j.1000-3673.pst.2016.02.027
Zhao Y, Gao Z, **ao Y et al (2019) Short-term load forecasting based on multilayer clustering and improved BP neural network. Eng J Wuhan Univ 52(07):622–629. https://doi.org/10.14188/j.1671-8844.2019-07-009
Peng X, He H, Yao J et al (2010) Method of bus-load forecasting using BP neural network optimized by PSO. Proc CSU-EPSA 22(05):146–151. https://doi.org/10.3969/j.issn.1003-8930.2010.05.025
Wang D, Wang L, Zhang G (2012) Short-term wind speed forecast model based on genetic BP neural network. J Zhejiang Univ (Eng Sci) 46(05):837–841+904. https://doi.org/10.3785/j.issn.1008-973X.2012.05.010
Shi J, Tan T, Guo J et al (2018) Multi-task learning based on deep architecture for various types of load forecasting in regional energy system integration. Power Grid Technol 42(03):698–707. https://doi.org/10.13335/j.1000-3673.pst.2017.2368
Heidari A, Khovalyg D (2020) Short-term energy use prediction of solar-assisted water heating system: application case of combined attention-based LSTM and time-series decomposition. Sol Energy 207:626–639. https://doi.org/10.1016/j.solener.2020.07.008
Chen Z, Liu J, Li C et al (2020) Ultra short-term power load forecasting based on combined LSTM-XGBoost model. Power Syst Technol 44(02):614–620. https://doi.org/10.13335/j.1000-3673.pst.2019.1566
** S, Xue H, Lin X, Zhang Z (2017) Study of short-term load forecasting of power system based on fuzzy deep neural network. J Qingdao Univ (Eng Technol Ed) 32(04):12–16. https://doi.org/10.13306/j.1006-9798.2017.04.003
Gong Y, Teng H (2019) Short-term load forecasting based on GOA-SVM. Electr Meas Instrum 56(14):12–14. https://doi.org/10.19753/j.issn1001-1390.2019.014.003
Shun L, Yuan F (2022) Research on stock price prediction based on orthogonal Gaussian basis function expansion and Pearson correlation coefficient weighted LSTM neural network. Adv Comput Signals Syst 6(5). https://doi.org/10.23977/acss.2022.060504
Hu C, Zhao Y, Jiang H, Jiang M et al (2022) Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN. Energy Rep 8(S8). https://doi.org/10.1016/j.egyr.2022.09.171
Zhang B, Song C, Jiang X et al (2023) Electricity price forecast based on the STL-TCN-NBEATS model. Heliyon 9(1). https://doi.org/10.1016/j.heliyon.2023.e13029
Wei J, Wu X, Yang T et al (2023) Ultra-short-term forecasting of wind power based on multi-task learning and LSTM. Int J Electr Power Energy Syst 149. https://doi.org/10.1016/J.IJEPES.2023.109073
Acknowledgements
This research is financially supported by Natural Science Foundation of Ningxia under grant 2022AAC03614 and the authors thank the reviewers for their constructive suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Qu, G. et al. (2024). Electric Heating Load Prediction Based on TCN-LSTM Hybrid Neural Network. In: Yadav, S., Arya, Y., Muhamad, N.A., Sebaa, K. (eds) Energy Power and Automation Engineering. ICEPAE 2023. Lecture Notes in Electrical Engineering, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-99-8878-5_33
Download citation
DOI: https://doi.org/10.1007/978-981-99-8878-5_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8877-8
Online ISBN: 978-981-99-8878-5
eBook Packages: EnergyEnergy (R0)