An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments (1603.08025v1)

Abstract: Smart energy in buildings is an important research area of Internet of Things (IoT). Buildings as important parts of the smart grids, their energy efficiency is vital for the environment and global sustainability. Using a LEED-gold-certificated green office building, we built a unique IoT experimental testbed for our energy efficiency and building intelligence research. We first monitor and collect one-year-long building energy usage data and then systematically evaluate and analyze them. The results show that due to the centralized and static building controls, the actual running of green buildings may not be energy efficient even though they may be "green" by design. Inspired by "energy proportional computing" in modern computers, we propose a IoT framework with smart location-based automated and networked energy control, which uses smartphone platform and cloud computing technologies to enable multi-scale energy proportionality including building-, user-, and organizational-level energy proportionality. We further build a proof-of-concept IoT network and control system prototype and carried out real-world experiments which demonstrate the effectiveness of the proposed solution. We envision that the broad application of the proposed solution has not only led to significant economic benefits in term of energy saving, improving home/office network intelligence, but also bought in a huge social implication in terms of global sustainability.

• The paper introduces an IoT framework that leverages location-aware controls and cloud computing to enhance dynamic energy management in smart buildings.
• Methodology includes real-world energy monitoring and offline modeling that reveal discrepancies between expected and actual energy efficiency in LEED-certified settings.
• Experimental results demonstrate up to a 60% reduction in energy consumption, underscoring the practical benefits of user-centered automation in energy systems.

A Framework for Smart Energy in Buildings Using IoT Technologies

The paper "An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments" presents a comprehensive paper focusing on improving energy efficiency in buildings through Internet of Things (IoT) technologies. Recognized as a crucial component of smart grids, buildings significantly contribute to energy consumption and carbon emissions, highlighting the necessity for effective energy management solutions. This paper outlines a novel IoT framework that leverages location-based automation and cloud computing to achieve multi-scale energy proportionality, which encompasses building-, organizational-, and user-level energy management.

Methodology and Framework

The authors implemented their research in a LEED-gold-certified office building, setting up an IoT testbed with real-world energy monitoring and control systems. Their framework addresses three primary aspects: energy monitoring, modeling and evaluation, and system implementation for practical energy management strategies.

1. Energy Monitoring: The testbed collected energy data from various building levels including rooms and individual occupants. This real-world data collection contrasts with simulation-based approaches, offering a richer and more actionable understanding of energy usage patterns.
2. Modeling and Evaluation: By conducting offline analysis, the researchers identified consumption patterns and key influencing factors such as occupancy and environmental conditions. Their findings revealed that actual energy performance often deviates from the expected efficiency of green buildings due to static and centralized control systems.
3. System Implementation: A pivotal contribution of the paper is the design and prototype of a location-aware IoT framework. Utilizing smartphone and cloud technologies, the system dynamically adjusts energy policies based on user locations, providing a flexible and efficient approach to energy control. This enables real-time interaction with building systems, facilitating energy saving through automated adjustments.

Numerical Results

The experimental results highlight significant energy savings potential. By employing real-time locational data and user-customized controls, the prototype demonstrated up to a 60% reduction in energy consumption compared to traditional static systems. These outcomes underscore the efficacy of adding dynamic control enabled by IoT technologies to existing building management systems.

Implications and Future Developments

The implications of this research extend beyond individual buildings, as the proposed framework can be tailored to various building types and scales, offering a modulable template for global application. On a practical level, the commercialization of such IoT solutions promises substantial economic benefits through reduced energy costs. Theoretically, it propels forward the paradigm of energy proportionality in smart grids, moving towards granular and user-specific energy management.

Future research directions may include extending the framework to incorporate machine learning algorithms for predictive energy management and integrating it with broader smart city initiatives to comprehensively optimize urban energy use. There is also potential for further exploring multisource policy integration in diverse organizational settings.

In conclusion, this paper contributes to the evolution of intelligent building systems by presenting a viable IoT-based solution for smarter energy management, encouraging a shift towards more interactive and user-centered energy control frameworks within the architecture of smart grids.