基于数据驱动环境中的统计理论和智能算法的绿色行为传播分析
Linhe Zhu1, Yi Ding1, Shuling Shen2
1School of Mathematical Sciences, Jiangsu University, Zhenjiang, PR China.
Mathematical biosciences
|November 21, 2024
概括
这项研究使用三层网络模拟绿色行为传播,分析信息扩散,意识和能源效率. 它采用微观马尔科夫链方法和反应扩散系统来理解和预测清洁能源的采用.
科学领域:
- 复杂系统科学 复杂系统科学
- 计算社会科学 计算社会科学
- 能源系统分析 能源系统分析
背景情况:
- 对能源效率的日益重视推动了对个人绿色行为的兴趣.
- 了解绿色行为传播的动态对于促进能源效率至关重要.
研究的目的:
- 开发一个分析信息传播,意识和绿色行为传播的模型.
- 研究绿色行为传播的时空动态.
- 预测清洁能源的产生和验证模型的有效性.
主要方法:
- 一个三层网络模型,包括信息扩散,意识和绿色行为.
- 微观马尔科夫链方法 (MMCA) 用于状态转移分析和值计算.
- 反应-扩散系统用于建模时空动态和识别图灵分叉.
- 最佳控制和卷积神经网络 (CNN) 用于参数识别.
- 与自行回归集成移动平均线 (ARIMA) 和其他神经网络进行能量生成预测的比较.
主要成果:
- 使用MMCA推导的状态转移方程和值.
- 确定了绿色行为传播的平衡点和图灵分叉标准.
- 通过数值模拟和参数识别验证模型.
- 基于CNN和最佳控制参数识别效率的比较.
- 预测了中国的电力发电量,并使用开发的模型来拟合清洁能源数据.
结论:
- 综合模型有效地捕捉了影响绿色行为和能源效率的因素的复杂相互作用.
- 该研究为分析和促进采用清洁能源发电等绿色行为提供了强有力的框架.
- 数字模拟和比较分析证实了模型的预测能力和应用方法的有效性.
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