摘要
能效分级是能效测评的基础,传统的能效分级方法单一且有局限性。文中提出一种智能化的评级方法,将神经网络应用于电力用户能效分析中,建立了基于神经网络的能效评级模型,从而不用给定某个特定的显式数学表达式。系统以RBF神经网络为核心建立模型,使用正交最小二乘法学习。综合考虑电能能效、电能污染能效和经济能效,可实时有效地进行能效分析及智能评级,并给出量化节能方案。仿真及实例计算表明,电力用户实时能效评级的RBF模型操作简捷、适用性强、实时性高,且具有较强的实用价值。
Energy efficiency grading is the base of energy efficiency evaluation, but there are many limitations of traditional methods. This paper presents an intelligent rating method by which the RBF neural network is applied to the efficiency assessment of the power user. And an energy efficiency rating model is established based on the RBF neural network saving the need for a particular explicit mathematical expression. The core of the model is the RBF neural network, which is trained using the Orthogonal Least Square algorithm, considering the energy efficiency, power quality pollution and economic efficiency. The trained system can quickly and effectively evaluate the grading standards of energy efficiency for power users, and propose the countermeasures. The example given in this paper shows that the RBF neural network model of energy efficiency assessment for power users is of high reliability, good applicability, and practical value.
出处
《电子科技》
2015年第6期1-4,共4页
Electronic Science and Technology
基金
国家自然科学基金资助项目(61205076)
关键词
电力用户
能效分级
RBF神经网络
power user
energy efficiency assessment
RBF neural network
