基于灰狼算法的多目标智能家居负荷控制算法

Multi Objective Flexible Load Scheduling in Smart Home Based on Grey Wolf Algorithm

为实现智能家居负荷智能控制,以电器用电量为依据,设计三级电价模型。设计多参数舒适度模型,将开关电器和温度控制电器分类建模,以智能设备收集的温湿度、光照强度、人体活动情况数据为基础进行负荷优化控制。采用灰狼算法对多目标问题进行求解,灰狼算法在求解高维、多峰的复杂函数问题有其优越性,理论和实验分析都证明灰狼算法在求解精度和稳定性均优于粒子群算法和差分进化算法等。经过实验仿真分析,提出的算法能够兼顾用户舒适度的同时有效降低电器用电量。

A three level electricity price model is designed based on the electricity consumption. A comfort model of multi parameters is designed for optimizing the grid load control of temperature and the humidity, light intensity and human activity. Grey wolf algorithm is used to solve the multi-objective problem. The grey wolf algorithm has its superiority in solving high dimensional and multi peak of complex function problems. Theoretical and experimental analysis shows that the grey wolf algorithm in accuracy and stability is better than particle swarm optimization algorithm and differential evolution algorithm. The simulation results show that the proposed algorithm can make user comfort and reduce the power consumption.