基于时间序列分解方法和灰色模型的季度电力消耗预测
1College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Environmental science and pollution research international
|August 6, 2023
概括
本研究引入了一种使用Loess (STL) 和灰色模型进行季节和趋势分解的新型季节性电力消耗预测方法. 该方法准确预测能源使用量,提高电力系统的稳定性和减少浪费.
科学领域:
- 能源系统分析 能源系统分析
- 时间序列预测时间序列预测
- 计算智能是一种计算智能.
背景情况:
- 准确的电力消耗预测对于电力系统的稳定性和废物减少至关重要.
- 非线性和季节性波动对传统预测模型构成挑战.
- 现有的方法很难有效地捕捉能源需求的季节性模式.
研究的目的:
- 开发一种新的电力消耗季节性预测方法.
- 为了应对能源数据非线性和季节性波动的挑战.
- 提高电力需求预测的准确性.
主要方法:
- 使用季节性和趋势分解使用Loess (STL) 进行时间序列分解.
- 将电力数据分解为趋势,季节性和剩余组件.
- 应用了灰色模型来预测具有明显特征的单个组件.
主要成果:
- 与最先进的方法相比,拟议的模型显示出更高的预测准确性.
- 获得了1.77%的平均绝对百分比误差 (MAPE) 和2.37%的根平均平方百分比误差 (RMSPE).
- 在季度电力消耗数据中成功识别和预测季节性波动.
结论:
- 新型STL和灰色模型方法有效预测季节性电力消耗.
- 这种方法为优化季节性能源供应方案提供了一个新的工具.
- 该模型的准确性提高了电力系统管理,减少了能源浪费.
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