智能家居的可持续能源管理框架

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Soteris Constantinou;Constantinos Costa;Andreas Konstantinidis;Panos K. Chrysanthis;Demetrios Zeinalipour-Yazti
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引用次数: 0

摘要

日益加剧的全球能源危机和不断增加的${\text{CO}_{2}}$排放使得优化能源效率成为必要。物联网(iot)设备的激增(预计到2030年将达到1000亿台)加剧了这场能源危机,并随后导致全球排放量增加。与此同时,气候和能源目标为在住宅中逐步采用太阳能光伏发电铺平了道路。将物联网集成到家庭能源管理系统中,有可能节省能源和峰值需求。由于用户定义的偏好和消费模式的复杂性,优化设备规划以减少${\text{CO}_{2}}$排放提出了重大挑战。在本文中,我们提出了一个创新的物联网数据平台,即可持续能源管理框架(SEMF),旨在平衡从电网进口的能源、用户的舒适度和排放之间的权衡。SEMF结合了绿色规划进化算法,创造了GreenCap$^+$+,以促进物联网设备的负载转移,同时考虑到可再生能源的整合、多种约束、高峰需求时间和动态定价。根据我们利用现实世界数据的实验评估,我们的原型系统比最先进的方法减少了大约29%的进口能源,大约35%的可再生能源自我消耗增加,大约34%的排放减少,同时保持了高水平的用户舒适度,大约94%-99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Sustainable Energy Management Framework for Smart Homes
The escalating global energy crisis and the increasing ${\text{CO}_{2}}$ emissions have necessitated the optimization of energy efficiency. The proliferation of Internet of Things (IoTs) devices, expected to reach 100 billion by 2030, contributed to this energy crisis and subsequently to the global ${\text{CO}_{2}}$ emissions increase. Concomitantly, climate and energy targets have paved the way for an escalating adoption of solar photovoltaic power generation in residences. The IoT integration into home energy management systems holds the potential to yield energy and peak demand savings. Optimizing device planning to mitigate ${\text{CO}_{2}}$ emissions poses significant challenges due to the complexity of user-defined preferences and consumption patterns. In this article, we propose an innovative IoT data platform, coined Sustainable Energy Management Framework (SEMF), which aims to balance the trade-off between the imported energy from the grid, users’ comfort, and ${\text{CO}_{2}}$ emissions. SEMF incorporates a Green Planning evolutionary algorithm, coined GreenCap$^+$+, to facilitate load shifting of IoT-enabled devices, taking into consideration the integration of renewable energy sources, multiple constraints, peak-demand times, and dynamic pricing. Based on our experimental evaluation utilizing real-world data, our prototype system has outperformed the state-of-the-art approach by up to $\approx$29% reduction in imported energy, $\approx$35% increase in self-consumption of renewable energy, and $\approx$34% decrease in ${\text{CO}_{2}}$ emissions, while maintaining a high level of user comfort $\approx$94%-99%.
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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