电力系统
计算机科学
数学优化
模糊逻辑
帕累托原理
灵活性(工程)
趋同(经济学)
可靠性工程
功率(物理)
工程类
人工智能
数学
经济
统计
物理
量子力学
经济增长
作者
Jianzhou Wang,Qianyi Xing,Bo Zeng,Weigang Zhao
出处
期刊:Applied Energy
[Elsevier]
日期:2022-12-01
卷期号:327: 120042-120042
被引量:17
标识
DOI:10.1016/j.apenergy.2022.120042
摘要
As an irreplaceable power source, electricity is responsible for sustaining the national economy and social development, and the precondition for the power system’s stable operation is to perform an accurate short-term load forecast (STLF). However, with the increasing forms of social power consumption and the emergence of large-scale sustainable resources on the grid, which make STLF increasingly challenging as the power load exhibits greater stochasticity and instability. Therefore, a novel STLF system is developed in this paper, which incorporates data fuzzy granulation, a high-performance optimizer for integrating forecasting sequences, point and interval forecasts. Moreover, the performance tests of the optimization algorithm verify that our proposed optimizer can obtain more approximate solution sets to the real Pareto front and outshines the traditional optimization algorithm concerning convergence and diversity. Load data from three regions of Australia demonstrate that our developed system can remarkably contribute to the accuracy and stability of the STLF, and also quantify the volatility and uncertainty of the power load, which allows power workers to better capture the fluctuation interval of future loads and effectively enhance the flexibility of grid operation.
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