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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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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