论文标题
基于ANFIS的组合循环发电厂的发电预测
ANFIS-based prediction of power generation for combined cycle power plant
论文作者
论文摘要
本文介绍了适应性神经模糊推理系统（ANFI）的应用，以预测合并的循环发电厂中产生的电力。 ANFIS体系结构是通过MATLAB通过的代码实现的，该代码利用混合算法结合了梯度下降和训练网络的最小平方估计器。通过将其应用于具有三个变量的非线性方程（时间序列Mackey-Glass方程和MATLAB中的ANFIS工具箱）来验证该模型。一旦确认其有效性，就会实施ANFI，以预测发电厂生成的电力。 ANFIS有三个输入：温度，压力和相对湿度。每个输入都被三个高斯成员功能构成。一阶Sugeno类型的脱水方法用于评估清晰的输出。拟议的Anfis是成功预测发电的电缆，其精度非常高，并且比工具箱快得多，这使其成为能源发电应用的有前途的工具。
This paper presents the application of an adaptive neuro-fuzzy inference system (ANFIS) to predict the generated electrical power in a combined cycle power plant. The ANFIS architecture is implemented in MATLAB through a code that utilizes a hybrid algorithm that combines gradient descent and the least square estimator to train the network. The Model is verified by applying it to approximate a nonlinear equation with three variables, the time series Mackey-Glass equation and the ANFIS toolbox in MATLAB. Once its validity is confirmed, ANFIS is implemented to forecast the generated electrical power by the power plant. The ANFIS has three inputs: temperature, pressure, and relative humidity. Each input is fuzzified by three Gaussian membership functions. The first-order Sugeno type defuzzification approach is utilized to evaluate a crisp output. Proposed ANFIS is cable of successfully predicting power generation with extremely high accuracy and being much faster than Toolbox, which makes it a promising tool for energy generation applications.
