%0 Journal Article
%T 基于自适应粒子群算法的智能家居管理系统负荷优化模型<br>Load optimization model of smart home management system based on adaptive particle swarm optimization
%A 马汉杰
%A 林霞
%A 胥晓晖
%A 张健
%A 张智晟&lt
%A br&gt
%A MA Hanjie
%A LIN Xia
%A XU Xiaohui
%A ZHANG Jian
%A ZHANG Zhisheng
%J 山东大学学报（工学版）
%D 2017
%R 10.6040/j.issn.1672-3961.0.2017.533
%X 摘要： 在能源互联网发展的背景下,针对电网需求侧响应的策略及用户节约用电成本的要求,设计智能家居管理系统(smart home management system, SHMS)的基本结构,构建智能家居管理系统负荷优化模型,并采用引入衰减因子的自适应粒子群算法对模型进行求解,可得到满足用户要求的家庭负荷运行方案。仿真算例采用了实际的分时电价、室外温度、负荷参数等信息,与优化前相比,用户负荷曲线得到改善,用电成本及用电量明显下降,验证了算法的有效性。<br>Abstract: In the situation of the development of Energy Internet, the basic structure of smart home management system(SHMS)was designed, the intelligent house management system load optimization model was constructed, which met the demand response strategy for power grid and the user's requirement to save the electricity cost. Particle swarm optimization(PSO)algorithm for introducing attenuation factors was used to solve the model, which got the program to meet the needs of users. The simulation results were based on the actual time-sharing price, outdoor temperature and load parameters. After the simulation, the user load curve was improved and the electricity consumption and energy used were obviously reduced, which proved the algorithm was effective
%K 负荷优化
%K 能源互联网
%K 自适应粒子群算法
%K 智能家居管理系统
%K &lt
%K br&gt
%K load optimization
%K smart home management system
%K adaptive PSO
%K Energy Internet
%U http://gxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1672-3961.0.2017.533