基于NARX神经网络的MATLAB使用印度尼西亚发电的短期预测
Nicholas Pranata1, Fahmy Rinanda Saputri1
1Department of Engineering Physics, Universitas Multimedia Nusantara, Jl. Scientia Boulevard Gading, Curug Sangereng, Serpong, Kabupaten Tangerang, Banten, Indonesia.
PloS one
|February 4, 2026
概括
由于需求的增加,准确的电力生产预测在印度尼西亚至关重要. 这项研究成功地使用了带有外源输入 (NARX) 的非线性自回归神经网络,实现了高精度的预测年度发电量.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
背景情况:
- 由于人口增长,城市化和技术进步,印尼化石燃料的电力消费,生产和供应增加,导致环境破坏.
- 准确的电力预测对于可持续的能源管理和减轻环境影响至关重要.
研究的目的:
- 用外源输入 (NARX) 非线性自回归神经网络预测印度尼西亚一年前的年度发电量.
- 为了比较莱文伯格-马奎特和贝叶斯规范化算法的性能,在NARX模型中进行电力发电预测.
主要方法:
- 应用非线性自回归与外源输入 (NARX) 神经网络模型用于电力生产预测.
- 使用MATLAB进行模型实现和预测.
- 采用70%-30%的数据分割用于培训和测试,具有30个隐藏层和2倍的步骤延迟.
主要成果:
- 莱文伯格-马奎特和贝叶斯规则化算法都实现了超过0.9.9的确定系数 (R2).
- 这两种算法的平均绝对百分比误差 (MAPE) 低于3%,表明预测准确度很高.
- 与贝叶斯规范化相比,莱文伯格-马奎特算法表现出略有优异的性能.
结论:
- NARX神经网络模型,特别是Levenberg-Marquardt算法,为印度尼西亚的短期年度发电预测提供了宝贵的见解.
- 该模型的高准确性支持能源规划和环境管理的知情决策.
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