通过反传播神经网络构建和优化计数系数的非参数分析模型
Yuqiang Yang1, Ruoyun Hu2, Weifeng Wang2
1State Grid Zhejiang Electric Power Co. Ltd, Hanzghou City, 310007, China.
Scientific reports
|May 20, 2024
概括
这项研究引入了一种新的非参数分析方法,使用逆向传播 (BP) 神经网络进行计数系数分析. 与传统方法相比,BP神经网络显著提高了准确性并减少了错误.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 传统的计数系数分析方法的准确性低,处理时间长.
- 现有的技术缺乏现代能源计量应用所需的效率和精度.
研究的目的:
- 提出和评估一种用于计数系数分析的新型非参数分析方法.
- 为了利用背向传播 (BP) 神经网络的分类和模式识别能力.
- 在准确性和处理时间方面克服传统方法的局限性.
主要方法:
- 使用英国家用电器电力数据集进行模型培训和测试.
- 使用非参数分析进行数据预处理,特征提取和规范化.
- 实现了反向传播 (BP) 神经网络模型,用于计数系数分析.
主要成果:
- 与最小平方方法 (LSM) 相比,拟议的BP神经网络模型显示了明显改善的准确性指标 (MAE,MRE).
- 在测试数据集上实现了0.025的平均绝对误差 (MAE) 和1.32%的平均相对误差 (MRE).
- 在相同的数据集上,LSM方法的结果是MAE为0.043和MRE为2.56%.
结论:
- 基于BP神经网络的非参数分析模型为计数系数分析提供了卓越的准确性.
- 这种新的方法为电力行业在能源计量和负载预测方面提供了实际参考.
- 该BP神经网络为传统方法提供了可行的替代方案,提高了效率和精度.
相关概念视频
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
50
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...
50
Transformers with Off-Nominal Turns Ratios
150
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
150
Mechanistic Models: Compartment Models in Individual and Population Analysis
37
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
37
Survival Tree
80
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
80
Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Regression Analysis
5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.7K


