Abstract
Accurate short-term load forecasting (STLF) is useful for power operators to respond to customers’ demand and to rationalize generation schedules to reduce renewable energy waste. However, there are still challenges to improve the accuracy of STLF: firstly, modeling the long-term relationships between past observations; secondly, the relationships between variables should be considered in modeling. For this reason, we propose temporal convolution and multi-attention (variable attention and temporal attention) (TC-MA) for electricity load forecasting. Since the electricity load forecast values show different trends influenced by historical loads and covariates, we use variable attention to obtain the dependencies between load values and covariates. The temporal dependence of covariates and loads are extracted separately by temporal convolution, and the temporal attention is then used to assign different weight values to each timestep. We validate the effectiveness of our method using three real datasets. The results show that our model performs excellent results compared to traditional deep learning models.
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References
Chen, K., Chen, K., Wang, Q., et al.: Short-term load forecasting with deep residual networks. IEEE Trans. Smart Grid 10(4), 3943–3952 (2018)
Bunn, D., Farmer, E.D.: Comparative models for electrical load forecasting (1985)
Chakhchoukh, Y., Panciatici, P., Mili, L.: Electric load forecasting based on statistical robust methods. IEEE Trans. Power Syst. 26(3), 982–991 (2010)
Charytoniuk, W., Chen, M.S.: Very short-term load forecasting using artificial neural networks. IEEE Trans. Power Syst. 15(1), 263–268 (2000)
Niu, D.X., Wanq, Q., Li, J.C.: Short term load forecasting model using support vector machine based on artificial neural network. In: 2005 International Conference on Machine Learning and Cybernetics, vol. 7, pp. 4260–4265. IEEE (2005)
Yun, Z., Quan, Z., Caixin, S., et al.: RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Trans. Power Syst. 23(3), 853–858 (2008)
Che, J.X., Wang, J.Z.: Short-term load forecasting using a kernel-based support vector regression combination model. Appl. Energy 132, 602–609 (2014)
Amjady, N.: Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Trans. Power Syst. 16(3), 498–505 (2001)
Kuo, P.H., Huang, C.J.: A high precision artificial neural networks model for short-term energy load forecasting. Energies 11(1), 213 (2018)
Siddarameshwara, N., Yelamali, A., Byahatti, K.: Electricity short term load forecasting using Elman recurrent neural network. In: 2010 International Conference on Advances in Recent Technologies in Communication and Computing, pp. 351–354 (2010). IEEE
Tasarruf, B., Chen, H.Y., et al.: Short-term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN. Energy Reports 8, 1678–1686 (2022). ISSN 2352-4847
Miao, K., Hua, Q., Shi, H.: Short-term load forecasting based on CNN-BiLSTM with Bayesian optimization and attention mechanism. In: Zhang, Y., Xu, Y., Tian, H. (eds.) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2020. LNCS, vol. 12606, pp. 116–128. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69244-5_10
Yasuno, T., Ishii, A., Amakata, M.: Rain-code fusion: code-to-code ConvLSTM forecasting spatiotemporal precipitation. In: Del Bimbo, A., et al. (eds.) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. LNCS, vol. 12667, pp. 20–34. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68787-8_2
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)
Liu, Y.F., Yang, Y.H.: Research on short-term power load forecasting based on CNN-LSTM. Sci. Technol. Innov. Appl. 1, 84–85 (2020)
Li, N., Wang, L., Li, X., et al.: An effective deep learning neural network model for short-term load forecasting. Concurr. Comput. Pract. Exp. 32(7), e5595 (2020)
Liang, Y., Wang, H., Zhang, W.: A knowledge-guided method for disease prediction based on attention mechanism. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds.) Web Information Systems and Applications. WISA 2022. LNCS, vol. 13579, pp. 329–340. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20309-1_29
Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant Nos. 62002262, 62172082, 62072086, 62072084, 71804123).
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Sun, C., Guo, H., Shen, D., Nie, T., Hou, Z. (2023). Temporal Convolution and Multi-Attention Jointly Enhanced Electricity Load Forecasting. In: Yuan, L., Yang, S., Li, R., Kanoulas, E., Zhao, X. (eds) Web Information Systems and Applications. WISA 2023. Lecture Notes in Computer Science, vol 14094. Springer, Singapore. https://doi.org/10.1007/978-981-99-6222-8_4
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DOI: https://doi.org/10.1007/978-981-99-6222-8_4
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