使用开发的人类进化优化算法和Xception神经网络进行电力使用预测

Dongxian Yu¹, Di Wu², Chongyang Liao³

¹College of Modern Information technology, Henan Polytechnic, Zhengzhou, 450046, Henan, China.

Scientific reports

|July 9, 2025

概括

这项研究提出了一种新的方法,用于预测使用电力的发达人类进化优化 (DHEO) 算法和Xception神经网络 (Xception-NN). 节能策略显著减少了消耗,特别是在工业部门.

科学领域:

人工智能的人工智能
能源系统能源系统
计算优化计算优化

背景情况:

准确的电力使用预测对于能源电网管理和资源分配至关重要.
传统的预测模型经常在能源消耗数据中与复杂的非线性模式作斗争.
深度学习和进化算法为提高预测准确性提供了潜力.

研究的目的:

引入一种新的混合模型,将Xception神经网络 (Xception-NN) 与开发的人类进化优化 (DHEO) 算法相结合,用于增强电力使用预测.
在基线和节能场景下分析电力消耗模式.
确定节能措施对最有影响的行业.

主要方法:

开发一个修改的Xception神经网络 (Xception-NN) 深度学习模型,用于时间序列的电力消耗数据.
应用开发的人类进化优化 (DHEO) 算法来改进Xception-NN参数以改进模式识别.
在基线 (BL) 和节能 (EC) 场景下模拟电力消耗,考虑天气,人口统计和经济等因素.

主要成果:

DHEO-Xception-NN模型在捕捉电力消耗的复杂依赖性和模式方面表现出更高的准确性.
节能 (EC) 场景导致电力消耗总体显著减少6.54%.
在EC情景下,工业部门的电力消耗下降幅度最大.

结论:

混合DHEO-Xception-NN方法为能源公司提供了一种强大的工具,以提高需求预测和优化能源生产.
节能举措可以大幅减少电力使用,对不同经济部门产生有针对性的影响.
这项研究为决策者和公用事业提供商提供了有价值的见解,旨在提高能源效率和管理未来的能源需求.

关键词:

开发了人类进化优化 (DHEO) 算法.电力消耗电力消耗优化优化优化优化预测预测预测 Xception-NNNNNNNNNNNXception-NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN

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