.png){kind=icon}
返回
Finite State Machine-Based Load Scheduling Algorithm for Smart Home Energy Management
.png){kind=icon}
PRE
AI
DOI：10.1080/03772063.2021.2010607.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The problem of scheduling household appliances with the availability of renewable energy is the biggest challenge in the smart home energy management system. The components such as renewable energy resources, household appliances, utility grid, storage batteries are pooled into a nonlinear, time-varying, indefinite, and dynamic structure that is impossible to control and refine. For this, real-time pricing is applied in most nations to withstand the burden on the grid. This requires attention to utilize renewable energy effectively. In this paper, a load scheduling method to schedule the loads based on the availability of solar energy and customer preferences is presented. First, the availability of solar energy is forecasted ahead one day using Regression Analysis. Second, the finite state machine approach-based load scheduling algorithm is implemented and tested using MATLAB Simulink and Lab VIEW. LabVIEW-based GUI is developed to visualize the MATLAB schedule for loads. The problem is divided into several states with the availability of solar power, and if solar power is unavailable, grid power is utilized. The loads preferred by the consumers are scheduled in alignment with the production of solar power with the finite state machine scheduling algorithm. Also, the loads considered are able to consume instantaneous energy with the instantaneous production of energy, thereby reducing CO2 emission by not consuming power from the grid. Finally, the loads are scheduled accordingly, and it is concluded that coordination can be established between energy providers, and the system proposed can flatten out the load profile.
Keyword:
Energy management
finite state machine
load scheduling
scheduling algorithm
simulink model
solar irradiance
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
1.3
论文数:
261
被引数:
3.3K
机构
引用论文
Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids微电网多智能体分散能量管理的模糊Q学习.png)
APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏Calcium imaging in live rat optic nerve myelinated axons in vitro using confocal laser microscopy
PREAI
分享收藏Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads需求侧管理: 需求响应、智能能源系统和智能负载
OAAI
OAAI分享收藏Axonal activation-induced calcium transients in myelinating Schwann cells, sources, and mechanisms
OAAI
OAAI分享收藏
查看更多内容