文献类型： 外文期刊

作者： Zhang, Mei ¹ ; Zhang, Danting ² ; Xie, Tingfeng ³ ;

作者机构： 1.Zhongkai Univ Agr & Engn, Coll Econ & Trade, Guangzhou 510225, Peoples R China

2.Guangdong Acad Agr Sci, Inst Agr Econ & Informat, Guangzhou 510640, Peoples R China

3.Govt Xinan Town, Chenghai Dist, Shantou 515800, Peoples R China

关键词： Urban energy transition; Smart homes; Environmental sustainability; Smart appliances

期刊名称：SUSTAINABLE CITIES AND SOCIETY （ 影响因子：11.7； 五年影响因子：10.6 ）

ISSN： 2210-6707

年卷期： 2024 年 101 卷

页码：

收录情况： SCI

摘要： This paper delves into the critical intersection of urban energy management, economic growth, and environmental sustainability through the integration of renewable energy sources in smart urban homes. The escalating demand for power has created an energy scarcity, necessitating innovative solutions to balance supply and consumption. In this context, the transition from traditional electric grids to smart grids (SG) is gaining prominence. To harness the potential of smart urban homes, particularly in the context of renewable energy, solar panels on rooftops have become indispensable. Smart grid technologies enable users to engage in demand response strategies, offering incentives and financial benefits for flexible device usage. This study introduces a novel optimization algorithm, the Polar Bear Optimization Algorithm (PBOA), designed to schedule domestic device usage patterns efficiently. To evaluate the effectiveness of PBOA, this paper conducts comparative analyses with other optimization algorithms such as Differential Evolution (DE) and Particle Swarm Optimization Algorithm (PSOA). Energy cost metrics, including Critical Peak Price (CPP) and Real -Time Price (RTP), are employed to assess cost reduction strategies. The primary focus of this study is to minimize energy costs and reduce the Peak to Average Ratio (PAR), thereby promoting economic growth and environmental sustainability in smart urban homes. It should be noted that neural network (NN) machine learning technique has been used for forecasting the weather and renewable energies output power.