Issue |
E3S Web Conf.
Volume 441, 2023
2023 International Conference on Clean Energy and Low Carbon Technologies (CELCT 2023)
|
|
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Article Number | 03019 | |
Number of page(s) | 11 | |
Section | Intelligent Ecological Management and Green Service | |
DOI | https://doi.org/10.1051/e3sconf/202344103019 | |
Published online | 07 November 2023 |
Research on Load Modeling Method for Typical Low Carbon Energy Consumption Scenarios in Border and Cross border Regions Considering Seasonal Migration Characteristics
1 China Southern Power Grid, Kunming, 650220, China
2 Institute of Building Environment and Energy Conservation, China Academy of Building Science, Beijing, 100013, China
* Corresponding author: Shukui Liang: sk9337@qq.com
With the process of urbanization and the ‘the Belt and Road’ initiative, the cross-border energy demand in southwest China has grown rapidly, driving the development of the energy system. The accuracy of load forecasting directly affects the application of energy systems, so it is crucial to conduct research on load forecasting for energy terminals in border and cross-border areas. However, there is a seasonal shift in the diverse energy consumption loads in border and cross-border regions, and currently, research on load forecasting and simulation of typical low-carbon energy consumption scenarios under this feature is basically in a blank state. Based on existing problems, this article conducts research on load modeling methods under the significant ‘seasonal migration’ characteristics of border and cross-border loads, conducts research on characteristic industries in border and cross-border areas, establishes typical low-carbon energy consumption scenarios and simulation models in border and cross-border areas, and uses sensitivity analysis method of dynamic simulation to analyze the impact of different influencing factors on the load of various building types, The Monte Carlo simulation prediction method is used to obtain the sensitivity probability distribution of various influencing characteristic factors, and the typical energy consumption building load model is modified. Finally, by comparing the energy consumption simulation results with statistical results, the accuracy of simulation energy consumption prediction is verified to be higher than 90%.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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