使用改进的红狐优化器 (IRFO) 优化人工神经网络的帮助预测运输能源需求
Yijie Liu1, Gongxing Yan2,3, Andrea Settanni4,5
1Chongqing Creation Vocational College, Yongchuan 402160, Chongqing, China.
Heliyon
|November 30, 2023
概括
本研究介绍了一种新的人工神经网络 (ANN) 模型,该模型与改进的红狐优化器 (IRFO) 进行了优化,用于预测运输能源需求. 根据经济和人口因素,ANN-IRFO模型在预测能源需求方面提供了卓越的准确性.
科学领域:
- 能源政策和规划 能源政策和规划
- 计算智能是一种计算智能.
- 可持续的运输可持续的运输
背景情况:
- 交通运输的能源需求对全球能源消耗和温室气体排放产生重大影响.
- 准确的预测对于有效的能源政策制定和实施至关重要.
- 了解GDP,人口和车辆数量等因素的复杂相互作用是关键.
研究的目的:
- 提出一种用于预测运输能源需求的新方法.
- 通过改进的红狐优化器 (IRFO) 利用人工神经网络 (ANN) 来提高预测准确性.
- 模拟能源需求与关键影响参数之间的非线性关系.
主要方法:
- 利用人工神经网络 (ANN) 模型捕捉复杂的非线性关系.
- 采用了改进的红狐优化器 (IRFO) 算法来优化ANN模型参数.
- 综合关键预测指标:国内生产总值 (GDP),人口和车辆数量.
主要成果:
- 与其他方法相比,拟议的ANN-IRFO模型显示出更高的准确性和有效性.
- 该模型准确地预测了增长率:GDP (5.5%),人口 (4.8%) 和车辆数量 (4.2%).
- 验证了模型精确预测运输能源需求的能力.
结论:
- ANN-IRFO方法提供了对运输能源需求的可靠预测.
- 调查结果支持对能源管理和可持续性的知情决策.
- 突出了人工智能驱动模型在应对能源挑战方面的潜力.
相关概念视频
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
01:01Production Efficiency
16.8K
Net production efficiency (NPE) is the efficiency at which organisms assimilate energy into biomass for the next trophic level. Due to low metabolic rates and less energy spent on thermoregulatory processes, the NPE of ectotherms (cold-blooded animals) is 10 times higher than endotherms (warm-blooded animals).
16.8K