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In order to improve the accuracy of ultra-short-term load forecasting, an ultra-short-term load forecasting model was proposed by an improved fireworks algorithm to optimize the extreme learning machine. Firstly, an improved fireworks algorithm was proposed by optimizing the mapping and mutation rules of the original fireworks algorithm. Then, the problem of model instability caused by the random formation of the weights and thresholds of the extreme learning machine was solved by using an improved fireworks algorithm to optimize the extreme learning machine. With the weight and threshold as variables, and the error coefficient of the extreme learning machine as the objective function, the optimal weight and threshold were sought by improved fireworks algorithm. Finally, the analysis of simulation data showed that the improved fireworks algorithm had faster convergence speed and better global optimization ability. Using the improved load forecasting model,the accuracy and stability of load forecasting had been greatly improved. The results showed that the model could be applied to ultra-short-term load forecasting of power systems in practical situations.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2020028
China Classification Code:TP18;TM715
Citation Information:
[1]JIANG Jiandong,SHI Yangtao,YAN Yuehao ,et al.Ultra-short-term Load Forecasting of Extreme Learning Machine Based on Improved Fireworks Algorithm[J].Journal of Zhengzhou University(Natural Science Edition),2020,52(04):110-115.DOI:10.13705/j.issn.1671-6841.2020028.
Fund Information:
国家自然科学基金项目(51507155)
2020-01-18
2020
2020-04-06
2020-10-05
2020
1
2020-04-20
2020-04-20
2020-04-20