Abstract
Accurate forecast of electricity sales is a very meaningful task for both electricity companies, security and government departments. This paper proposes a forecasting model for short-term, mid-term and long-term electricity sales respectively. For short-term and mid-term forecasting, we use multiple base models to make predictions and use model fusion methods to get the final prediction results. As for long-term forecasting, the tuned Prophet is used to make a prediction. Through experiments, we found that XGBoost as the base model can achieve the best prediction effect, in which the short-term prediction error can reach 1.97% and the average error of the mid-term prediction is 1.472%. In the long-term forecasting, the MAPE of the entire month of June 2020 is 3.64%. It can be seen that they have achieved good prediction results.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61803391, and in part by the Hunan Provincial Natural Science Foundation of China under Grant No. 2019JJ50803.
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Li, K. et al. (2021). Electricity Sales Forecasting Based on Model Fusion and Prophet Model. In: Cheng, J., Tang, X., Liu, X. (eds) Cyberspace Safety and Security. CSS 2020. Lecture Notes in Computer Science(), vol 12653. Springer, Cham. https://doi.org/10.1007/978-3-030-73671-2_26
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DOI: https://doi.org/10.1007/978-3-030-73671-2_26
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