摘要
在家庭智能用电系统下,以经济性和舒适性为目标,构建了电动汽车、空调、热水器的优化用电模型。并使用基于Q学习的粒子群算法求解优化模型,阐述家用电器的智能用电策略。以空调负荷为例,采用优化模型和算法后,经仿真实验,满足温度控制要求,且费用最少,收敛速度快,有效减少了空调负荷的用电量,削减电费的同时又保证用户的舒适度。
The optimization model of electric vehicle, air conditioner, water-heater is instituted based on the household intelligent power utilization system, with the goal of economy and comfort. The particle swarm optimization based on Qlearning is used to solve the optimization model, so the intelligent power strategy of household electric appliances is solved. With the optimization model and the algorithm of air conditioner, and through the simulation experiment, the room temperature is controlled, the cost is least and the convergence rate is fast, so the electricity consumption of air conditioner load is reduced and the comfort of the consumer is guaranteed.
作者
张晓芳
谢俊
ZHANG Xiaofang;XIE Jun(School of Electrical Engineering, Suzhou Chien-shiung Institute of Technology, Taicang, Jiangsu 215411, China;College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)
出处
《计算机工程与应用》
CSCD
北大核心
2016年第24期246-250,共5页
Computer Engineering and Applications
基金
2015年太仓市重点研发计划(产业前瞻与共性关键技术)项目(No.TC2015GY13)
关键词
家用电器
优化模型
粒子群算法
Q学习算法
家庭智能用电系统
household appliances
optimization model
particle swarm optimization
Q-learning algorithm
household intelligent power utilization system
