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23 November 2022 Research on investment demand forecasting model of provincial power grid
Shuo Wang, Jian Wang, Jia Liu, Yunfei Liu, Yijiang Liu, Huijuan Huo, Dan Xu
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Proceedings Volume 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022); 1230247 (2022) https://doi.org/10.1117/12.2645807
Event: Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 2022, Guangzhou, China
Abstract
As an important implementation subject of the national energy strategy, the power grid undertakes the important responsibility of optimizing the allocation of energy resources and serving economic and social development. With the rapid development of China's national economy, the power demand of the whole society is gradually increasing. Under the background of building a new power system, the investment demand of China's power grid is increasing. Accurate and effective prediction of power grid investment demand can not only help to coordinate the funds, reasonably arrange the investment of power grid construction funds and reduce economic risks, but also effectively improve the operation status of power grid enterprises and improve the investment income. Based on the internal and external environment and development status of power grid investment, this paper puts forward five influencing factors affecting investment demand, and uses SSA-LSSVM algorithm to predict the investment trend of h power grid from 2021 to 2023. The results show that the investment in h power grid will increase steadily year by year after 2020.
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Shuo Wang, Jian Wang, Jia Liu, Yunfei Liu, Yijiang Liu, Huijuan Huo, and Dan Xu "Research on investment demand forecasting model of provincial power grid", Proc. SPIE 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 1230247 (23 November 2022); https://doi.org/10.1117/12.2645807
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KEYWORDS
Data modeling
Power supplies
Optimization (mathematics)
Computer programming
Carbon
Detection and tracking algorithms
Process modeling
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