A Sustainable Energy Management Framework for Smart Homes

Abstract

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%.