开发一种混合技术来预测能源需求,该技术基于由增强的多层优化器优化改进的SVM:对影响因素的调查
Anzhong Huang1, Qiuxiang Bi2, Luote Dai3
1School of Accounting and Finance, Anhui xinhua University, Hefei, 230088, Anhui , China.
这项研究引入了一种先进的中国电力需求预测模型,大大提高了预测准确度. 新方法通过更好地捕捉复杂的需求影响,提高了能源资源管理.
科学领域:
- 能源系统 能源系统
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 准确的电力需求预测对于高效的能源资源管理和战略规划至关重要.
- 现有的预测模型往往难以捕捉影响电力需求的复杂,非线性关系.
- 需要强大的模型,能够处理需求和影响变量之间的复杂相互作用.
研究的目的:
- 为中国的电力需求预测开发一种新的多形式模型.
- 解决目前在建模非线性需求动态方面的方法的局限性.
- 提高电力需求预测的可靠性和准确性.
主要方法:
- 集成升级的支持矢量机 (SVM) 与改进的基因算法用于内核功能增强.
- 应用增强多节优化器 (BMVO) 来优化模型权重和改善概括性.
- 测试拟议的BMVO/增量SVM (ISVM) 模型,使用中国实际能源需求数据.
主要成果:
- 拟议的BMVO/ISVM模型与现有方法相比,显示出更高的可靠性和精度.
- 最佳的预测准确度是通过1735个选定样本实现的,以最低的平均绝对百分比误差 (MAPE) 表示.
- 与人工神经网络 (ANN) 方法相比,观察到错误指标的显著减少:根平均平方错误 (RMSE) 减少了53.72%,MAPE减少了55.22%.
结论:
- 开发的BMVO/ISVM模型准确地预测了中国的电力需求.
- 模型的有效性受到预测过程中使用的样本数量的影响.
- 这种方法有可能在中国以外的各种能源需求预测场景中应用.
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