一个新的季节性灰色预测模型,用于能源预测的分数顺序积累
1Resource Environment and Regional Economic Development Research Center, Xi'an University of Finance and Economics, Xi'an, 710100, China.
Heliyon
|May 2, 2024
概括
一个新的季节性灰色模型 (FSGM) 通过将数据重启和分数订单积累相结合,准确地预测季节性数据. 该模型的性能优于预测电力发电和石油焦炭生产的现有方法.
科学领域:
- 时间序列分析时间序列分析
- 预测建模预测建模
- 能源经济学 能源经济学
背景情况:
- 对具有季节性模式的顺序数据的准确预测对于能源管理和生产规划至关重要.
- 现有的灰色模型经常难以捕捉季节性数据固有的动态趋势.
- 需要提高能源发电和工业产出的预测准确度,需要新的建模方法.
研究的目的:
- 引入一个新的季节性灰色模型,FSGM (1,1,),集成数据重启技术和分数顺序积累.
- 为了优化FSGM (1,1,) 模型,使用粒子群优化 (PSO) 对于分数顺序和背景值系数.
- 通过案例研究验证FSGM (1,1,) 的有效性,并预测未来的能源和生产趋势.
主要方法:
- 使用分数顺序积累的季节性灰色模型的开发 (FSGM (1,1,).
- 粒子群优化 (PSO) 的应用用于模型参数估计.
- 使用开发的FSGM (1,1,) 模型预测季度发电和石油焦炭生产.
主要成果:
- FSGM (1,1,) 模型在捕获季节性数据动态方面表现出有效性,并提供准确的预测.
- 与传统的灰色模型 (GM (1,1),SGM (1,1),DGGM (1,1),DGSM (1,1),DGSTM (1,1)) 相比,FSGM (1,1,) 在数据拟合和预测准确性方面表现出更好的表现.
- 预测显示,北京的电力发电稳定,河南的电力发电和中国的石油焦炭产量从2023年至2027年稳步增加,季节性波动显著.
结论:
- 拟议的FSGM (1,1,) 是用于预测季节性时间序列数据的强大而有效的工具.
- 该模型的性能优于现有的季节性灰色模型的能力凸显了其在能源和工业预测中的实际应用.
- 未来的趋势表明河南发电和中国石油焦炭产量的持续增长,强调了适应性规划的必要性.
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