| Issue |
E3S Web Conf.
Volume 360, 2022
2022 8th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2022)
|
|
|---|---|---|
| Article Number | 01079 | |
| Number of page(s) | 19 | |
| DOI | https://doi.org/10.1051/e3sconf/202236001079 | |
| Published online | 23 November 2022 | |
Load forecasting analysis for regional and industry power systems-based on ARIMA model and LSTM model
1 School of Economics, Jinan University, Guangzhou 510632, China
2 School of Management, Jinan University, Guangzhou 510632, China
3 International College of Jinan University, Guangzhou 510632, China
* Corresponding author: tliuxd@jnu.edu.cn
The forecasting and analysis for the power system load profoundly affect the security of electricity for production and daily life. Based on the load data of 15- minute interval in a regional power grid in China, this paper forecasts the load of 15 - minute interval in the next 10 days, the maximum values of daily load in the next 3 months, and the maximum and minimum values of daily load in the next 3 months for each industry in the region on the basis of the ARIMA model and the LSTM model respectively, and then compares the forecasting effects of the two models. The conclusions show that (1) The LSTM model has a higher forecasting accuracy than that of the ARIMA model in general. (2) Daily electricity load levels are higher for large industry and commerce and lower for non-general industry and general industry. (3) The sudden changes of electricity load in each industry occur mainly on holidays and days with rainy weather. And the magnitude of the sudden change in the load of large industries is the largest. Finally, innovative suggestions are made for different industries in the context of the “Double Carbon Plan”.
Key words: Load forecasting / Load abrupt change / ARIMA model / LSTM model
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.