一种基于遗传编程的组合方法,用于长期预测电力需求
Hayat Ahmed Issa1, Hasan Hüseyin Çevik2, Ahmet Yilmaz3
1Electrical & Electronics Engineering, Institute of Science, Selcuk University, Selcuklu, Konya, Türkiye.
PeerJ. Computer science
|June 26, 2025
概括
这项研究提出了一种使用遗传编程预测埃塞俄比亚到2031年的电力消耗的新型组合方法. 该先进技术预计,到2031年,电力使用量将比2021年的水平增加三倍.
科学领域:
- 能源经济学 能源经济学
- 计算智能是一种计算智能.
- 预测科学 预测科学
背景情况:
- 准确的长期电力消耗预测对于埃塞俄比亚的能源基础设施规划至关重要.
- 现有的预测模型可能无法捕捉到埃塞俄比亚日益增长的能源需求的复杂动态.
研究的目的:
- 开发和验证一种新的整体方法,用于长期预测埃塞俄比亚的电力消耗.
- 预测到2031年的电力消费趋势.
主要方法:
- 采用了两阶段的整体方法,将遗传算法 (GA),粒子群优化 (PSO) 和模拟化 (SA) 与回归模型 (线性,二次性,指数) 集成在一起.
- 基因编程 (GP) 在第二阶段被用来改进初步预测,创建最终的预测模型.
- 使用平均绝对百分比误差 (MAPE),平均平方误差 (MSE),根平均平方误差 (RMSE) 和R平方 (R2) 来评估性能.
主要成果:
- 初步阶段取得了准确的预测,GA_Quadratic,PSO_Quadratic和SA_Quadratic的MAPE值分别为3.61%,3.63%和4.68%.
- 基于遗传编程的最终组合模型实现了2.83%的优异MAPE.
- 拟议的模型在所有评估的错误指标中超过了所有第一阶段方法.
结论:
- 基于基因编程的新型组合方法为预测长期电力消耗提供了非常准确的方法.
- 根据各种情景,埃塞俄比亚的电力消耗预计到2031年将比2021年的水平增加三倍.
- 这种预测模型可以在埃塞俄比亚的战略能源规划和基础设施开发中发挥重要作用.
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