A hybrid forecasting model for monthly electricity consumption combining prophet and SARIMAX models

Jie Ma; Yuelong Jia; Binglin Lv

doi:10.1117/12.3005085

9 October 2023 A hybrid forecasting model for monthly electricity consumption combining prophet and SARIMAX models

Jie Ma, Yuelong Jia, Binglin Lv

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127912J (2023) https://doi.org/10.1117/12.3005085
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

This paper proposes a weighted hybrid forecasting model that combines the strengths of Prophet and SARIMAX models for predicting monthly electricity consumption. The case study focuses on northern China's city B, utilizing monthly electricity consumption and temperature data from 2010 to 2019. The weighted hybrid model achieves superior performance compared to individual Prophet and SARIMAX models, with Mean Absolute Percentage Error (MAPE) reduced by 22.8% and 19.0% respectively. The weighted hybrid model effectively integrates the advantages of both forecasting methods and significantly improves prediction accuracy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jie Ma, Yuelong Jia, and Binglin Lv "A hybrid forecasting model for monthly electricity consumption combining prophet and SARIMAX models", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127912J (9 October 2023); https://doi.org/10.1117/12.3005085

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