Prediction of electricity consumption in Anhui Province based on HP-ARIMA-BP

Zedong Zhou; Xiangnan Jing; Yaqi Li; Boshan Nan; Huazhi Yu; Ling Wang; Yu Zhu; Yu Cao

doi:10.1117/12.2671443

29 March 2023 Prediction of electricity consumption in Anhui Province based on HP-ARIMA-BP

Zedong Zhou, Xiangnan Jing, Yaqi Li, Boshan Nan, Huazhi Yu, Ling Wang, Yu Zhu, Yu Cao

Proceedings Volume 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022); 125940Y (2023) https://doi.org/10.1117/12.2671443
Event: Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 2022, Xi'an, China

Abstract

China has a large electricity consumption with a large proportion of people living in the country, and the electricity consumption and generation in diverse regions are not balanced. The forecast of electricity load and short-term energy consumption can be helpful in easing the long-term electricity consumption shortage. To optimize the power dispatching strategy of each province and city, we must pay attention to the short-term forecast of regional power load. In this paper, a new short-term power load forecasting HP-ARIMA-BP model is used to screen and count relevant power data from all over the country. Use LSTM to fill in the missing values of time series, with HP filter decomposition to decompose electricity consumption in Anhui Province, remove trend items and volatility interference in the data, apply ARIMA to predict the trend items, and use BP neural network to predict the volatility items. The final fit achieves high accuracy.

Citation Download Citation

Zedong Zhou, Xiangnan Jing, Yaqi Li, Boshan Nan, Huazhi Yu, Ling Wang, Yu Zhu, and Yu Cao "Prediction of electricity consumption in Anhui Province based on HP-ARIMA-BP", Proc. SPIE 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 125940Y (29 March 2023); https://doi.org/10.1117/12.2671443

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KEYWORDS

Data modeling

Power consumption

Tunable filters

Neural networks

Autoregressive models

Machine learning

Performance modeling

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